Federated Learning from Molecules to Processes: A Perspective | Paper Citation Record

Observation 562278f7-ed0e-423b-849a-a5767250a01d · outbound

This paper cites Schweidtmann, Erik Esche, Asja Fischer, Marius Kloft, Jens-Uwe Repke, Sebastian Sager, and Alexander Mitsos.

Federated Learning from Molecules to Processes: A Perspective Schweidtmann, Erik Esche, Asja Fischer, Marius Kloft, Jens-Uwe Repke, Sebastian Sager, and Alexander Mitsos

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:56.993485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b27c931b-938c-47d1-91f8-48b27e77c8a6 · outbound

This paper cites an unresolved cited work.

Federated Learning from Molecules to Processes: A Perspective Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:56.999789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79098267-3abb-451a-b59f-7257d33f43ce · outbound

This paper cites Lee, Srinivas Rangarajan, Leo Chiang, Bhushan Gopaluni, Artur M.

Federated Learning from Molecules to Processes: A Perspective Lee, Srinivas Rangarajan, Leo Chiang, Bhushan Gopaluni, Artur M

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.004719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e67806c-c59f-44ad-b289-fb3c5f7b8351 · outbound

This paper cites How to do impactful research in artificial intelligence for chemistry and materials science.

Federated Learning from Molecules to Processes: A Perspective How to do impactful research in artificial intelligence for chemistry and materials science

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.010079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f4bc769-e887-4881-acb2-0d75ff95f0de · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Federated Learning from Molecules to Processes: A Perspective Deep Learning Scaling is Predictable, Empirically

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.015197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f0749dd-cbdc-4652-a25d-b4f805539a5c · outbound

This paper cites Scaling Laws for Neural Language Models.

Federated Learning from Molecules to Processes: A Perspective Scaling Laws for Neural Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.020470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bc1c649-780e-4f92-bb9d-c9af0fe8a1a2 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Federated Learning from Molecules to Processes: A Perspective Training Compute-Optimal Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.026624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3197aa68-2769-4a45-a68d-f838f2bee213 · outbound

This paper cites Scaling vision transformers.

Federated Learning from Molecules to Processes: A Perspective Scaling vision transformers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.031892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7257970-bbdd-4548-a883-64ff7b0e18e9 · outbound

This paper cites Advances and opportunities in machine learning for process data analytics.

Federated Learning from Molecules to Processes: A Perspective Advances and opportunities in machine learning for process data analytics

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.037145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f339627d-5b3a-429f-ba66-0a88c8b99849 · outbound

This paper cites Maximizing informa- tion from chemical engineering data sets: Applications to machine learning.

Federated Learning from Molecules to Processes: A Perspective Maximizing informa- tion from chemical engineering data sets: Applications to machine learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.042505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 091fb33a-ad00-4856-a452-a80eadb9b38a · outbound

This paper cites an unresolved cited work.

Federated Learning from Molecules to Processes: A Perspective Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.047971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6937aa21-ddcd-40a3-bff3-190887485e79 · outbound

This paper cites Federated learning in chemical engineering: A tutorial on a framework for privacy-preserving collaboration across distributed data sources.

Federated Learning from Molecules to Processes: A Perspective Federated learning in chemical engineering: A tutorial on a framework for privacy-preserving collaboration across distributed data sources

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.053077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b0aa0a9-1337-4db9-9c8e-0e74ca600b07 · outbound

This paper cites The sampl2 blind prediction challenge: introduction and overview.

Federated Learning from Molecules to Processes: A Perspective The sampl2 blind prediction challenge: introduction and overview

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.059285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46980896-e917-44a5-bddd-95de5e1e66bc · outbound

This paper cites Freesolv: a database of experimental and calculated hydration free energies, with input files.

Federated Learning from Molecules to Processes: A Perspective Freesolv: a database of experimental and calculated hydration free energies, with input files

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.064892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2698eb4-75cf-414c-b10e-70208163566a · outbound

This paper cites Dral, Matthias Rupp, and O.

Federated Learning from Molecules to Processes: A Perspective Dral, Matthias Rupp, and O

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.070044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e19ae195-dd44-4ad0-b35b-a1a83170be46 · outbound

This paper cites Summit: benchmarking machine learning methods for reaction optimisation.

Federated Learning from Molecules to Processes: A Perspective Summit: benchmarking machine learning methods for reaction optimisation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.075332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54634abf-29a7-47b0-98cc-97f3a524f850 · outbound

This paper cites Orderly: data sets and benchmarks for chemical reaction data.

Federated Learning from Molecules to Processes: A Perspective Orderly: data sets and benchmarks for chemical reaction data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.079998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a36de57-af2c-49fc-ba53-5752c6d7d059 · outbound

This paper cites Fault detection and diagnosis in industrial systems.

Federated Learning from Molecules to Processes: A Perspective Fault detection and diagnosis in industrial systems

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.086304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e839f70-a543-470f-814b-82823d35eb25 · outbound

This paper cites Perspectives on the integration between first-principles and data-driven modeling.

Federated Learning from Molecules to Processes: A Perspective Perspectives on the integration between first-principles and data-driven modeling

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.092047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eebd11ed-69f7-41d9-bde9-2f4c7bd40ca4 · outbound

This paper cites A review and perspective on hybrid modeling methodologies.

Federated Learning from Molecules to Processes: A Perspective A review and perspective on hybrid modeling methodologies

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.097951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1e495f8-3e32-4695-a8a3-af8d72228f04 · outbound

This paper cites Physics- informed machine learning.

Federated Learning from Molecules to Processes: A Perspective Physics- informed machine learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.102645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e015180-b1ed-40ad-a0aa-7fa56e692435 · outbound

This paper cites Autonomous chemical research with large language models.

Federated Learning from Molecules to Processes: A Perspective Autonomous chemical research with large language models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.108199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f39030f0-a7a1-4571-9a70-c679b3137dda · outbound

This paper cites Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller.

Federated Learning from Molecules to Processes: A Perspective Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.112865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66e5d4c4-0ed7-43ba-adc2-50ccbe397295 · outbound

This paper cites Transfer learning for solvation free energies: From quantum chemistry to experiments.

Federated Learning from Molecules to Processes: A Perspective Transfer learning for solvation free energies: From quantum chemistry to experiments

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.118199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d175380-654b-4aa9-8784-14490069b165 · outbound

This paper cites Fault detection and diagnosis based on transfer learning for multimode chemical processes.

Federated Learning from Molecules to Processes: A Perspective Fault detection and diagnosis based on transfer learning for multimode chemical processes

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.122889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72731e9b-c56a-48b1-98b7-1ae03aed1c69 · outbound

This paper cites Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes.

Federated Learning from Molecules to Processes: A Perspective Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.128258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 333161fe-b43d-49db-ad98-e580dd832bb8 · outbound

This paper cites Multi-fidelity data-driven design and analysis of reactor and tube simulations.

Federated Learning from Molecules to Processes: A Perspective Multi-fidelity data-driven design and analysis of reactor and tube simulations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.133214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09c5c846-85fd-4fec-b435-fe38001a161d · outbound

This paper cites Multi-fidelity graph neural networks for predicting toluene/water partition coefficients.

Federated Learning from Molecules to Processes: A Perspective Multi-fidelity graph neural networks for predicting toluene/water partition coefficients

Reference 28

Resolution: verified exact; doi, observed 2026-08-15T18:51:58.190283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 191ee797-a08a-489a-a184-063d69ddde09 · outbound

This paper cites Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas.

Federated Learning from Molecules to Processes: A Perspective Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.144731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1fcfd72-745b-4043-8215-de00ddb673d3 · outbound

This paper cites Brendan McMahan, Brendan Avent, Aurelien Bellet, and Sen Zhao.

Federated Learning from Molecules to Processes: A Perspective Brendan McMahan, Brendan Avent, Aurelien Bellet, and Sen Zhao

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.149774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e062d992-ab5a-4374-a0ba-aaae45c44ac3 · outbound

This paper cites Federated Learning in Practice: Reflections and Projections.

Federated Learning from Molecules to Processes: A Perspective Federated Learning in Practice: Reflections and Projections

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.154936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31cd28e8-8aea-4cc5-8ba0-96110ccb2c3f · outbound

This paper cites Recent advances on federated learning: A systematic survey.

Federated Learning from Molecules to Processes: A Perspective Recent advances on federated learning: A systematic survey

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.160095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91506800-ef06-4e5b-ad27-67d4ba2f9319 · outbound

This paper cites A survey on federated learning: challenges and applications.

Federated Learning from Molecules to Processes: A Perspective A survey on federated learning: challenges and applications

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.165011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da75d912-a499-4577-abd4-6eae3aaf5b02 · outbound

This paper cites H Ngai, and Thiemo V oigt.

Federated Learning from Molecules to Processes: A Perspective H Ngai, and Thiemo V oigt

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.169978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b706ec0a-26ce-45bb-b51f-c53b757738b1 · outbound

This paper cites The future of digital health with federated learning.

Federated Learning from Molecules to Processes: A Perspective The future of digital health with federated learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.174774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f87a6bb-ca55-42f7-a0af-fc5b609efab0 · outbound

This paper cites Nguyen, Quoc-Viet Pham, Pubudu N.

Federated Learning from Molecules to Processes: A Perspective Nguyen, Quoc-Viet Pham, Pubudu N

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.179863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 800c5034-d5b1-49d6-b4ab-24d3b9bf03c8 · outbound

This paper cites Rawat, and Vladimir Vlassov.

Federated Learning from Molecules to Processes: A Perspective Rawat, and Vladimir Vlassov

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.184936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f13c39f3-2464-40b2-a6d8-63d6404c734f · outbound

This paper cites Göller, Yves Moreau, Mathieu N.

Federated Learning from Molecules to Processes: A Perspective Göller, Yves Moreau, Mathieu N

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.189929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49878795-b5d7-47f5-a5d5-e8ccc5a306dd · outbound

This paper cites Applied Federated Learning: Improving Google Keyboard Query Suggestions.

Federated Learning from Molecules to Processes: A Perspective Applied Federated Learning: Improving Google Keyboard Query Suggestions

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.194913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7160d00-9b89-4c6b-8d35-3f13801b31d9 · outbound

This paper cites The Future of Large Language Model Pre-training is Federated.

Federated Learning from Molecules to Processes: A Perspective The Future of Large Language Model Pre-training is Federated

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.200477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1a0cb69-bfc1-43fc-812a-5550127a7963 · outbound

This paper cites Federated learning for computational pathology on gigapixel whole slide images.

Federated Learning from Molecules to Processes: A Perspective Federated learning for computational pathology on gigapixel whole slide images

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.205695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e99e5322-b9ee-414e-80bb-559cb4ed7a40 · outbound

This paper cites Federated learning for medical image analysis: A survey.

Federated Learning from Molecules to Processes: A Perspective Federated learning for medical image analysis: A survey

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.210871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8310c22f-67a7-49d2-8d78-3e4673f64214 · outbound

This paper cites Conformal efficiency as a metric for comparative model assessment befitting federated learning.

Federated Learning from Molecules to Processes: A Perspective Conformal efficiency as a metric for comparative model assessment befitting federated learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.215754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2de27719-7f4b-4439-9d7b-e275817995bc · outbound

This paper cites Industry-scale orchestrated federated learning for drug discovery.

Federated Learning from Molecules to Processes: A Perspective Industry-scale orchestrated federated learning for drug discovery

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.220692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6211b270-fb97-4ae1-bd54-2cc7064c561a · outbound

This paper cites Communication-efficient federated learning via knowledge distillation.

Federated Learning from Molecules to Processes: A Perspective Communication-efficient federated learning via knowledge distillation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.225494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 198854d0-4602-4d61-89d5-e3d9238c7cc9 · outbound

This paper cites Anger, Chris Barber, Richard J.

Federated Learning from Molecules to Processes: A Perspective Anger, Chris Barber, Richard J

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.230311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a36eb324-5efc-48c4-a34b-ad588667ff9b · outbound

This paper cites Decentralized and incentivized federated learning for the chemical engineering domain.

Federated Learning from Molecules to Processes: A Perspective Decentralized and incentivized federated learning for the chemical engineering domain

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.235112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b71d09c-c5da-4b51-b957-d860d1354bda · outbound

This paper cites Privacy-preserving federated machine learning modeling and predictive control of heterogeneous nonlinear systems.

Federated Learning from Molecules to Processes: A Perspective Privacy-preserving federated machine learning modeling and predictive control of heterogeneous nonlinear systems

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.239992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bc00515-ee0f-4cb1-908c-d3d6006809ca · outbound

This paper cites A review of federated learning in energy systems.

Federated Learning from Molecules to Processes: A Perspective A review of federated learning in energy systems

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.244964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 690052d6-55ef-4e4c-9166-b3ec50572a1e · outbound

This paper cites A review of federated learning in renewable energy applica- tions: Potential, challenges, and future directions.

Federated Learning from Molecules to Processes: A Perspective A review of federated learning in renewable energy applica- tions: Potential, challenges, and future directions

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.251277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af26b841-d48c-4550-bcea-fe0080b60832 · outbound

This paper cites an unresolved cited work.

Federated Learning from Molecules to Processes: A Perspective Unresolved cited work

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.264086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e61194e-8744-405f-ad35-105b80c5fa4f · outbound

This paper cites Review of Mathematical Optimization in Federated Learning.

Federated Learning from Molecules to Processes: A Perspective Review of Mathematical Optimization in Federated Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.270639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9f1611a-08e8-469e-99c6-7076f440beb9 · outbound

This paper cites Talwalkar.

Federated Learning from Molecules to Processes: A Perspective Talwalkar

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.276038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2a77dd0-a99d-4502-8a24-13f6a3e0977e · outbound

This paper cites Model aggregation techniques in federated learning: A comprehensive survey.

Federated Learning from Molecules to Processes: A Perspective Model aggregation techniques in federated learning: A comprehensive survey

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.281275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9618fdfd-cd9e-4531-8921-43647a28171c · outbound

This paper cites Dinh, Tung T.

Federated Learning from Molecules to Processes: A Perspective Dinh, Tung T

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.286261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ae71d60-1c20-49ea-a49b-fbf99995dc68 · outbound

This paper cites A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency.

Federated Learning from Molecules to Processes: A Perspective A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.291941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1da8a5ff-ba91-402d-8162-47af99de69ac · outbound

This paper cites On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data.

Federated Learning from Molecules to Processes: A Perspective On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.297519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2745a2c5-a94c-4e14-b766-98ec6b14d8c2 · outbound

This paper cites Thermodynamics-Consistent Graph Neural Networks.

Federated Learning from Molecules to Processes: A Perspective Thermodynamics-Consistent Graph Neural Networks

Reference 59

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T18:51:58.487971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 42df5d36-7560-41c1-a50f-f0a856370d04 · outbound

This paper cites Digitization of chemical process flow diagrams using deep convolutional neural networks.

Federated Learning from Molecules to Processes: A Perspective Digitization of chemical process flow diagrams using deep convolutional neural networks

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.307802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32aa0c8d-fa98-47b0-95f5-8a1d3562ddcb · outbound

This paper cites Flowsheet generation through hierarchi- cal reinforcement learning and graph neural networks.

Federated Learning from Molecules to Processes: A Perspective Flowsheet generation through hierarchi- cal reinforcement learning and graph neural networks

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.312630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db1c0a09-3a25-4f99-867f-cfa793041dd5 · outbound

This paper cites an unresolved cited work.

Federated Learning from Molecules to Processes: A Perspective Unresolved cited work

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.319910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8342c341-b9ba-4df5-a907-822ffa6fb26b · outbound

This paper cites A digitization and conversion tool for imaged drawings to intelligent piping and instrumentation diagrams (P&ID).

Federated Learning from Molecules to Processes: A Perspective A digitization and conversion tool for imaged drawings to intelligent piping and instrumentation diagrams (P&ID)

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.325076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6406cd26-0f0f-4222-831e-6fff4d924e41 · outbound

This paper cites Transforming engineering diagrams: A novel approach for P&ID digitization using transformers.

Federated Learning from Molecules to Processes: A Perspective Transforming engineering diagrams: A novel approach for P&ID digitization using transformers

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.330515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39676c7f-da53-4756-b01f-55f78f8ddf25 · outbound

This paper cites Schweidtmann.

Federated Learning from Molecules to Processes: A Perspective Schweidtmann

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.335856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8d207e2-4bdb-4f23-8b4e-0ff663459099 · outbound

This paper cites Advances in surrogate based modeling, feasibility analysis, and optimization: A review.

Federated Learning from Molecules to Processes: A Perspective Advances in surrogate based modeling, feasibility analysis, and optimization: A review

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.342150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6266f42d-0d15-44b9-9d02-b8bf97304114 · outbound

This paper cites Deterministic global optimization with artificial neural networks embedded.

Federated Learning from Molecules to Processes: A Perspective Deterministic global optimization with artificial neural networks embedded

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.349917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 153efb78-b7aa-430d-ab86-522f8b89354f · outbound

This paper cites Overview of surrogate modeling in chemical process engineering.

Federated Learning from Molecules to Processes: A Perspective Overview of surrogate modeling in chemical process engineering

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.357930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a260908-ebbd-4ff2-8ba0-a14a6f9bc146 · outbound

This paper cites Architectures for neural networks as surrogates for dynamic systems in chemical engineering.

Federated Learning from Molecules to Processes: A Perspective Architectures for neural networks as surrogates for dynamic systems in chemical engineering

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.363770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de8cc320-31ad-4a1d-9068-07a63d4a7866 · outbound

This paper cites Formulating data-driven surrogate models for process optimization.Computers & Chemical Engineering, 179:108411, 2023.

Federated Learning from Molecules to Processes: A Perspective Formulating data-driven surrogate models for process optimization.Computers & Chemical Engineering, 179:108411, 2023

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.369122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53146ac5-ffb7-4eaf-bf16-a7ce7b6f5408 · outbound

This paper cites Recent trends on hybrid modeling for industry 4.0.

Federated Learning from Molecules to Processes: A Perspective Recent trends on hybrid modeling for industry 4.0

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.374315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f985d0d-e25a-4deb-ad44-6cf324d834ea · outbound

This paper cites Machine learning for chemical reactions.

Federated Learning from Molecules to Processes: A Perspective Machine learning for chemical reactions

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.379317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0315b97-3e71-4418-b3ff-32af6c10a14c · outbound

This paper cites Exploring catalytic reaction networks with machine learning.

Federated Learning from Molecules to Processes: A Perspective Exploring catalytic reaction networks with machine learning

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.384573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaea6b25-2a99-4d0b-9eee-18b41b8f3be2 · outbound

This paper cites Chemical data intelligence for sustainable chemistry.

Federated Learning from Molecules to Processes: A Perspective Chemical data intelligence for sustainable chemistry

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.390065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1e936bc-2587-4df7-aff6-834d5c617806 · outbound

This paper cites Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies.

Federated Learning from Molecules to Processes: A Perspective Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.395093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4017c88f-3daf-4122-bca1-f779a13880b4 · outbound

This paper cites Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates.

Federated Learning from Molecules to Processes: A Perspective Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.400027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d985cf55-20da-4279-89db-b126daaed89d · outbound

This paper cites An artificial neural network approach to recognise kinetic models from experimental data.

Federated Learning from Molecules to Processes: A Perspective An artificial neural network approach to recognise kinetic models from experimental data

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.404726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffa70615-8bef-4665-9592-51392ecd6583 · outbound

This paper cites Generative artificial intelligence in chemical engineering.

Federated Learning from Molecules to Processes: A Perspective Generative artificial intelligence in chemical engineering

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.409403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c16490-91c1-4e18-a265-52fd7116b67d · outbound

This paper cites Automated synthesis of steady-state continuous processes using reinforcement learning.

Federated Learning from Molecules to Processes: A Perspective Automated synthesis of steady-state continuous processes using reinforcement learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.414003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac201b78-4e31-434a-9199-eebe6915999e · outbound

This paper cites Schweidtmann.

Federated Learning from Molecules to Processes: A Perspective Schweidtmann

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.418648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9854729a-e5ee-4951-8987-a6ce89db764c · outbound

This paper cites esfiles: Intelligent process flowsheet synthesis using process knowledge, symbolic ai, and machine learning.

Federated Learning from Molecules to Processes: A Perspective esfiles: Intelligent process flowsheet synthesis using process knowledge, symbolic ai, and machine learning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.423509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f91c362a-6b66-43fc-bac0-7d9b3c544dab · outbound

This paper cites Schweidtmann.

Federated Learning from Molecules to Processes: A Perspective Schweidtmann

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.428731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bb549f7-5d83-48fb-8223-1a755c9817c4 · outbound

This paper cites Graph-to-sfiles: Control structure prediction from process topologies using generative artificial intelligence.

Federated Learning from Molecules to Processes: A Perspective Graph-to-sfiles: Control structure prediction from process topologies using generative artificial intelligence

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.433815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7867bb46-340d-4c5c-a184-fbe100982a9f · outbound

This paper cites Deep reinforcement learning for process design: Review and perspective.

Federated Learning from Molecules to Processes: A Perspective Deep reinforcement learning for process design: Review and perspective

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.438959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 739ff514-2180-4635-9315-8bce3f7761f0 · outbound

This paper cites Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge.

Federated Learning from Molecules to Processes: A Perspective Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.444109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dc26ffb-888f-4a54-8a47-5e02690c5f90 · outbound

This paper cites Data-driven control: Overview and perspectives.

Federated Learning from Molecules to Processes: A Perspective Data-driven control: Overview and perspectives

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.449413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c66ae6c-5e30-49e4-b487-3f102a5e11f9 · outbound

This paper cites System identification: A machine learning perspective.

Federated Learning from Molecules to Processes: A Perspective System identification: A machine learning perspective

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.455723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1103335-5415-4ac1-92ad-801bde597f8f · outbound

This paper cites Comparative study of machine learning and system identification for process systems engineering dynamics.Industrial & Engineering Chemistry Research, 2025.

Federated Learning from Molecules to Processes: A Perspective Comparative study of machine learning and system identification for process systems engineering dynamics.Industrial & Engineering Chemistry Research, 2025

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.461143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d308097f-b85c-403e-9850-93a82069efa7 · outbound

This paper cites Process control via artificial neural networks and reinforcement learning.

Federated Learning from Molecules to Processes: A Perspective Process control via artificial neural networks and reinforcement learning

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.466902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42da0cbb-6243-4b21-b793-0b4ab7ad1421 · outbound

This paper cites Reinforcement learning–overview of recent progress and implications for process control.

Federated Learning from Molecules to Processes: A Perspective Reinforcement learning–overview of recent progress and implications for process control

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.472554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f92da903-7ae6-4d1d-b29c-fcf69b7cd320 · outbound

This paper cites A review on reinforcement learning: Introduction and applications in industrial process control.

Federated Learning from Molecules to Processes: A Perspective A review on reinforcement learning: Introduction and applications in industrial process control

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.478667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a51825a4-c1ff-4fbc-a297-3a62e854689a · outbound

This paper cites Recent advances in reinforcement learning for chemical process control.

Federated Learning from Molecules to Processes: A Perspective Recent advances in reinforcement learning for chemical process control

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.485530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b175bb9-ad42-4d15-ba10-a43d7c948148 · outbound

This paper cites Modeling and predictive control of nonlinear processes using transfer learning method.

Federated Learning from Molecules to Processes: A Perspective Modeling and predictive control of nonlinear processes using transfer learning method

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.490666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71f9b1cd-94ab-4616-a514-79b62f4bc0cc · outbound

This paper cites Hedengren.

Federated Learning from Molecules to Processes: A Perspective Hedengren

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.495792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde9fb51-858a-4e8c-aa63-41f12e60efef · outbound

This paper cites Optimization-based multi-source transfer learning for modeling of nonlinear processes.

Federated Learning from Molecules to Processes: A Perspective Optimization-based multi-source transfer learning for modeling of nonlinear processes

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.500805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b7bf70d-c881-4af0-95f4-3d0cfe72fdfa · outbound

This paper cites Control strategies for microgrids with distributed energy storage systems: An overview.

Federated Learning from Molecules to Processes: A Perspective Control strategies for microgrids with distributed energy storage systems: An overview

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.506350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 834c2014-773a-470e-827b-d3730cdc9dec · outbound

This paper cites Cooperative optimal power flow with flexible chemical process loads.

Federated Learning from Molecules to Processes: A Perspective Cooperative optimal power flow with flexible chemical process loads

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.510905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f360032-18a1-43fe-9ab8-8ad7c4db8dd9 · outbound

This paper cites Toward distributed energy services: Decentralizing optimal power flow with machine learning.IEEE Transactions on Smart Grid, 11(2):1296–1306, 2019.

Federated Learning from Molecules to Processes: A Perspective Toward distributed energy services: Decentralizing optimal power flow with machine learning.IEEE Transactions on Smart Grid, 11(2):1296–1306, 2019

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.516234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4deb51b-6689-48c8-9164-815bd70b886a · outbound

This paper cites Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources.

Federated Learning from Molecules to Processes: A Perspective Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.521382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23d2f18b-8ed4-4dd4-9b2f-3ff8ca80f22b · outbound

This paper cites Federated multiagent deep reinforcement learning approach via physics-informed reward for multimicrogrid energy management.

Federated Learning from Molecules to Processes: A Perspective Federated multiagent deep reinforcement learning approach via physics-informed reward for multimicrogrid energy management

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.526396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c7cf81-282c-4c5b-ab65-abd01f26dbdf · outbound

This paper cites Survey on ai and machine learning techniques for microgrid energy management systems.

Federated Learning from Molecules to Processes: A Perspective Survey on ai and machine learning techniques for microgrid energy management systems

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-15T18:51:57.531457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers