Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment | Paper Citation Record

Observation d7c809d8-840e-4f27-b145-73a9cd4108f3 · outbound

This paper cites Multi-Fidelity Reinforcement Learning Framework for Shape Optimization,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Multi-Fidelity Reinforcement Learning Framework for Shape Optimization,

Reference 1

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Observation 80fde0f6-ded7-4962-b0f0-fcaaf80fdf62 · outbound

This paper cites Multi-Fidelity Optimization of a Quiet Propeller Based on Deep Deterministic Policy Gradient and Transfer Learning,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Multi-Fidelity Optimization of a Quiet Propeller Based on Deep Deterministic Policy Gradient and Transfer Learning,

Reference 2

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This paper cites Reinforcement Learning for Efficient Design Space Exploration With Variable Fidelity Analysis Models,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Reinforcement Learning for Efficient Design Space Exploration With Variable Fidelity Analysis Models,

Reference 3

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This paper cites Multifidelity Reinforcement Learning with Control Variates,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Multifidelity Reinforcement Learning with Control Variates,

Reference 4

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This paper cites Leveraging Deep Reinforcement Learning for Design Space Exploration with Multi-Fidelity Surrogate Model,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Leveraging Deep Reinforcement Learning for Design Space Exploration with Multi-Fidelity Surrogate Model,

Reference 5

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This paper cites On the Use of Surrogate Models in Engineering Design Optimization and Exploration: The Key Issues,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment On the Use of Surrogate Models in Engineering Design Optimization and Exploration: The Key Issues,

Reference 6

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This paper cites A Review of Surrogate Modeling Techniques for Aerodynamic Analysis and Optimization: Current Limitations and Future Challenges in Industry,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment A Review of Surrogate Modeling Techniques for Aerodynamic Analysis and Optimization: Current Limitations and Future Challenges in Industry,

Reference 7

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Observation 431dfc44-6ed2-454e-a13e-c57e59251249 · outbound

This paper cites Review of multi-fidelity models.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Review of multi-fidelity models

Reference 8

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This paper cites Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization,

Reference 9

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Observation f5d2f08a-1ae0-43c5-aa84-0614ee31dbf4 · outbound

This paper cites Surrogate Modeling: Tricks That Endured the Test of Time and Some Recent Developments,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Surrogate Modeling: Tricks That Endured the Test of Time and Some Recent Developments,

Reference 10

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Observation 6b0524e5-0b81-4f57-9950-d7d1659a1c18 · outbound

This paper cites A Multi-Fidelity Surrogate Modeling Method in the Presence of Non-Hierarchical Low-Fidelity Data,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment A Multi-Fidelity Surrogate Modeling Method in the Presence of Non-Hierarchical Low-Fidelity Data,

Reference 11

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Observation cb429255-c7a0-4a8c-aad1-eeff0d58ff9e · outbound

This paper cites Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low- Fidelity Datasets,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low- Fidelity Datasets,

Reference 12

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Observation 5cc74505-3d2a-4edd-90c9-45a13d04b11a · outbound

This paper cites A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling,

Reference 13

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Observation 850b0de1-e48e-48d7-8aae-0dfbca87cd59 · outbound

This paper cites Deriving Metamodels to Relate Machine Learning Quality to Design Repository Characteristics in the Context of Additive Manufacturing,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Deriving Metamodels to Relate Machine Learning Quality to Design Repository Characteristics in the Context of Additive Manufacturing,

Reference 14

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Observation 12392b1f-76d0-4c81-b58f-1e88c5abddb1 · outbound

This paper cites Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing,

Reference 15

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Observation 6f3057d8-8bdc-47ed-b32a-1bb92cfcc9d3 · outbound

This paper cites Comparing Attribute- and Form-Based Machine Learning Techniques for Component Prediction,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Comparing Attribute- and Form-Based Machine Learning Techniques for Component Prediction,

Reference 16

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This paper cites Fairness- and Uncertainty- Aware Data Generation for Data-Driven Design,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Fairness- and Uncertainty- Aware Data Generation for Data-Driven Design,

Reference 17

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Observation fc499f4a-7763-40e6-a317-2c334e4fa9c4 · outbound

This paper cites Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation,

Reference 18

Resolution: verified exact; doi, observed 2026-08-12T19:18:07.785806Z

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Observation 7eb5ead8-67b1-4022-8783-6adbf31f7fc0 · outbound

This paper cites Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations,

Reference 19

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This paper cites https://doi.org/10.1007/978-981-15-4095-0.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment https://doi.org/10.1007/978-981-15-4095-0

Reference 20

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This paper cites S., and Barto, A.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment S., and Barto, A

Reference 21

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Observation a34866e9-3793-4e74-afcb-56b3e1ec5991 · outbound

This paper cites Human-Level Control through Deep Reinforcement Learning,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Human-Level Control through Deep Reinforcement Learning,

Reference 22

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This paper cites Mastering the Game of Go with Deep Neural Networks and Tree Search,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Mastering the Game of Go with Deep Neural Networks and Tree Search,

Reference 23

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This paper cites Discrete Structural Design Synthesis: A Hierarchical-Inspired Deep Reinforcement Learning Approach Considering Topological and Parametric Actions,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Discrete Structural Design Synthesis: A Hierarchical-Inspired Deep Reinforcement Learning Approach Considering Topological and Parametric Actions,

Reference 24

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Observation a6cebffa-68cb-4f9f-9eb5-0e2148a85d84 · outbound

This paper cites Reimagining Space Layout Design through Deep Reinforcement Learning,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Reimagining Space Layout Design through Deep Reinforcement Learning,

Reference 25

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Observation d3a119b7-d782-4bc0-9e07-050a35cf170a · outbound

This paper cites Reinforcement Learning for Engineering Design Automation,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Reinforcement Learning for Engineering Design Automation,

Reference 26

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Observation c88ce3c6-7d6a-4206-b136-4ca83ee68814 · outbound

This paper cites https://doi.org/10.1016/j.matdes.2022.110672.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment https://doi.org/10.1016/j.matdes.2022.110672

Reference 27

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This paper cites A Deep Reinforcement Learning Approach for Global Routing,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment A Deep Reinforcement Learning Approach for Global Routing,

Reference 28

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Observation d8e9c0ef-5e40-4df2-bad9-cb1db310f817 · outbound

This paper cites GCP-HOLO: Generating High- Order Linkage Graphs for Path Synthesis,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment GCP-HOLO: Generating High- Order Linkage Graphs for Path Synthesis,

Reference 29

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This paper cites Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs,

Reference 30

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This paper cites Modular Robot Design Synthesis with Deep Reinforcement Learning,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Modular Robot Design Synthesis with Deep Reinforcement Learning,

Reference 31

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Observation 249e28c2-99b8-421d-9fc1-8928698a8aae · outbound

This paper cites Learning to Design Without Prior Data: Discovering Generalizable Design Strategies Using Deep Learning and Tree Search,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Learning to Design Without Prior Data: Discovering Generalizable Design Strategies Using Deep Learning and Tree Search,

Reference 32

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This paper cites A Cost-Aware Multi-Agent System for Black-Box Design Space Exploration,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment A Cost-Aware Multi-Agent System for Black-Box Design Space Exploration,

Reference 33

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Observation 6626dba7-219e-4089-b354-a795740d4b8d · outbound

This paper cites A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting,

Reference 34

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This paper cites Framework for Design Optimization Using Deep Reinforcement Learning,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Framework for Design Optimization Using Deep Reinforcement Learning,

Reference 35

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Observation 13fa10ed-2dfb-42d1-895f-28f68f98a1aa · outbound

This paper cites Analyzing Real Options and Flexibility in Engineering Systems Design Using Decision Rules and Deep Reinforcement Learning,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Analyzing Real Options and Flexibility in Engineering Systems Design Using Decision Rules and Deep Reinforcement Learning,

Reference 36

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Observation b6bffa37-6dc4-4911-9db9-276e042d8851 · outbound

This paper cites The Computational Limits of Deep Learning,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment The Computational Limits of Deep Learning,

Reference 37

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This paper cites Between Progress and Potential Impact of AI: the Neglected Dimensions.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Between Progress and Potential Impact of AI: the Neglected Dimensions

Reference 38

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Observation c6e06ad3-1263-4185-b73f-86a94a73265e · outbound

This paper cites On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimization.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimization

Reference 39

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This paper cites Nonhierarchical Multi‐model Fusion Using Spatial Random Processes,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Nonhierarchical Multi‐model Fusion Using Spatial Random Processes,

Reference 40

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Observation 5bf61bb4-99b7-48a0-b918-857ab6e8e800 · outbound

This paper cites Predicting the Output from a Complex Computer Code When Fast Approximations Are Available,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Predicting the Output from a Complex Computer Code When Fast Approximations Are Available,

Reference 41

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This paper cites Reinforcement Learning with Multi-Fidelity Simulators,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Reinforcement Learning with Multi-Fidelity Simulators,

Reference 42

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This paper cites Multifidelity Reinforcement Learning with Gaussian Processes: Model-Based and Model-Free Algorithms,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Multifidelity Reinforcement Learning with Gaussian Processes: Model-Based and Model-Free Algorithms,

Reference 43

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This paper cites Toward Multi-Fidelity Reinforcement Learning for Symbolic Optimization,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Toward Multi-Fidelity Reinforcement Learning for Symbolic Optimization,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:08.823063Z

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This paper cites Low- Cost Multi-Agent Navigation via Reinforcement Learning with Multi-Fidelity Simulator,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Low- Cost Multi-Agent Navigation via Reinforcement Learning with Multi-Fidelity Simulator,

Reference 45

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 46

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This paper cites Proximal Policy Optimization Algorithms.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Proximal Policy Optimization Algorithms

Reference 47

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Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Unresolved cited work

Reference 49

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Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Unresolved cited work

Reference 50

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Observation c0b0a35b-ac06-4aaa-b0d9-e3501e50ceda · outbound

This paper cites A High-Fidelity Simulation Test-Bed for Fault-Tolerant Octo-Rotor Control Using Reinforcement Learning,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment A High-Fidelity Simulation Test-Bed for Fault-Tolerant Octo-Rotor Control Using Reinforcement Learning,

Reference 51

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Observation bb711b53-0013-4667-b64c-f370c0c26e62 · outbound

This paper cites A Flight Dynamics Model for Exploring the Distributed Electrical EVTOL Cyber Physical Design Space,.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment A Flight Dynamics Model for Exploring the Distributed Electrical EVTOL Cyber Physical Design Space,

Reference 52

Resolution: verified exact; raw_fallback, observed 2026-08-12T19:18:08.040007Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 16a2e52a-21db-4d4a-af60-9dde86e8d0bb · outbound

This paper cites an unresolved cited work.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Unresolved cited work

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T19:18:07.612688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a904266c-c2af-459b-897f-9001b88d91d2 · outbound

This paper cites an unresolved cited work.

Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment Unresolved cited work

Reference 482

Resolution: unresolved; no resolver link, observed 2026-08-12T19:18:07.463964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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