Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy | Paper Citation Record

Observation 1d0448ee-916c-47ae-8fc6-4994a8a2308f · outbound

This paper cites Banerjee, J.C.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Banerjee, J.C

Reference 1

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Observation f187c541-6486-43d8-8492-8769c52d3ff8 · outbound

This paper cites Boyer, An overview on the use of titanium in the aerospace industry, Mater.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Boyer, An overview on the use of titanium in the aerospace industry, Mater

Reference 2

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Observation 45816214-e295-48dd-86eb-b316cb7712f1 · outbound

This paper cites https://doi.org/10.1007/978-3-540-73036-1.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy https://doi.org/10.1007/978-3-540-73036-1

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Observation f2ac5cf3-efba-46c6-8a8d-de8d2c49fd0f · outbound

This paper cites Peters, J.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Peters, J

Reference 4

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Observation 1307b1e3-0e15-495d-a49b-d498a67188de · outbound

This paper cites Leyens, M.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Leyens, M

Reference 5

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Observation 778eeb13-38a5-4ecd-af69-c9c1599d0b6a · outbound

This paper cites Mahadule, R.K.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Mahadule, R.K

Reference 6

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Observation fa64a6e5-be08-4181-a37e-04372d1516f6 · outbound

This paper cites Joshi, D.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Joshi, D

Reference 7

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Observation 8b3cc5de-4257-425d-8cf6-bf15ebc7b046 · outbound

This paper cites Mahadule, P.S.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Mahadule, P.S

Reference 8

Resolution: verified exact; doi, observed 2026-08-02T05:14:19.517072Z

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Observation 40e6d51d-2cae-4f81-8da2-371862f505ba · outbound

This paper cites Sellars, W.J.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Sellars, W.J

Reference 9

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Observation bee75802-07a4-4423-bfa5-3ec6b8e1c2da · outbound

This paper cites Zener, J.H.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Zener, J.H

Reference 10

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This paper cites Garofalo, An empirical relation defining the stress dependence of minimum creep rate in metals, (1963) 351.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Garofalo, An empirical relation defining the stress dependence of minimum creep rate in metals, (1963) 351

Reference 11

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Unresolved cited work

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Observation 05f04a1b-b619-4508-b212-10aa90848fa8 · outbound

This paper cites Jonas, C.M.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Jonas, C.M

Reference 13

Resolution: verified exact; doi, observed 2026-08-02T05:14:18.654749Z

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Observation 86c4c5a0-de8a-4019-9f20-3f6db95c73cc · outbound

This paper cites Sakai, J.J.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Sakai, J.J

Reference 14

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Observation ed337571-6f32-444d-985c-2b945f0a79ed · outbound

This paper cites Cook, W.H.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Cook, W.H

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This paper cites Poliak, J.J.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Poliak, J.J

Reference 16

Resolution: verified exact; doi, observed 2026-08-02T05:14:18.082264Z

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Observation e68a05df-3368-4676-a0e0-15b57f234058 · outbound

This paper cites Seshacharyulu, S.C.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Seshacharyulu, S.C

Reference 17

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This paper cites an unresolved cited work.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Unresolved cited work

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Unresolved cited work

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Observation a0fb6f6b-6eb6-43a6-a0d9-ce0fe5e4ae29 · outbound

This paper cites Lin, X.-M.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Lin, X.-M

Reference 20

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This paper cites Safari, M.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Safari, M

Reference 21

Resolution: verified exact; doi, observed 2026-08-02T05:14:16.778977Z

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Observation d5beea3d-840a-4b88-88b0-be4277fd7071 · outbound

This paper cites Joshi, D.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Joshi, D

Reference 22

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This paper cites Ghaboussi, J.H.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Ghaboussi, J.H

Reference 23

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This paper cites Huber, Ch.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Huber, Ch

Reference 24

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This paper cites Sun, W.D.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Sun, W.D

Reference 25

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Unresolved cited work

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This paper cites Peng, K.L.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Peng, K.L

Reference 27

Resolution: verified exact; doi, observed 2026-08-02T05:14:16.197791Z

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This paper cites Sabokpa, A.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Sabokpa, A

Reference 28

Resolution: verified exact; doi, observed 2026-08-02T05:14:16.022665Z

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This paper cites Fan, H.C.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Fan, H.C

Reference 29

Resolution: verified exact; doi, observed 2026-08-02T05:14:15.901690Z

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Observation 9c61a118-2d36-43fb-bda3-eca75d737255 · outbound

This paper cites Logarzo, G.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Logarzo, G

Reference 30

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This paper cites Pandya, C.C.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Pandya, C.C

Reference 31

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Mozaffar, R

Reference 32

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This paper cites Gorji, M.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Gorji, M

Reference 33

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This paper cites Raissi, P.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Raissi, P

Reference 34

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This paper cites Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 36

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This paper cites Haghighat, M.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Haghighat, M

Reference 37

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This paper cites Mao, A.D.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Mao, A.D

Reference 38

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This paper cites Zhang, M.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Zhang, M

Reference 39

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Jagtap, K

Reference 40

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This paper cites Karniadakis, I.G.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Karniadakis, I.G

Reference 41

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Unresolved cited work

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This paper cites Vanishing Stacked-Residual PINN for State Reconstruction of Hyperbolic Systems.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Vanishing Stacked-Residual PINN for State Reconstruction of Hyperbolic Systems

Reference 43

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Observation 451fb644-8230-464e-a606-91dcb98223a3 · outbound

This paper cites LeCun, Y.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy LeCun, Y

Reference 44

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This paper cites Heaton, Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618, Genet.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Heaton, Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618, Genet

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Layer Normalization

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Searching for Activation Functions

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Decoupled Weight Decay Regularization

Reference 48

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 49

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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 50

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This paper cites Ghahramani, Zoubin Yarin, Dropout as a Bayesian approximation: representing model uncertainty in deep learning, (2016) 1050–1059.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Ghahramani, Zoubin Yarin, Dropout as a Bayesian approximation: representing model uncertainty in deep learning, (2016) 1050–1059

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This paper cites Salakhutdinov, Ruslan Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya, Dropout: a simple way to prevent neural networks from overfitting, (2014) 1929–1958.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Salakhutdinov, Ruslan Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya, Dropout: a simple way to prevent neural networks from overfitting, (2014) 1929–1958

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This paper cites Avrami, Kinetics of Phase Change.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Avrami, Kinetics of Phase Change

Reference 53

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Observation 9160fa93-5634-4363-b25b-597572ee7f95 · outbound

This paper cites Reaction Kinetics in Processes of Nucleation and Growth.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Reaction Kinetics in Processes of Nucleation and Growth

Reference 54

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Observation 9c33b9f7-fb48-48c2-bdd1-91b3e1946631 · outbound

This paper cites an unresolved cited work.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Unresolved cited work

Reference 56

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This paper cites Sakai, A.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Sakai, A

Reference 57

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Observation e6814ee3-428c-4693-81bc-69bdf65a16b4 · outbound

This paper cites Hallberg, Approaches to Modeling of Recrystallization, Metals 1 (2011) 16 –48.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Hallberg, Approaches to Modeling of Recrystallization, Metals 1 (2011) 16 –48

Reference 58

Resolution: verified exact; doi, observed 2026-08-02T05:14:14.858084Z

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This paper cites Ding, Z.X.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Ding, Z.X

Reference 59

Resolution: verified exact; doi, observed 2026-08-02T05:14:14.664690Z

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This paper cites Caruana, Multitask Learning, Mach.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Caruana, Multitask Learning, Mach

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-02T05:11:09.751174Z

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This paper cites Dodge, S.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Dodge, S

Reference 61

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Observation cb5d1fcb-8d4d-4662-92e2-b1a74db00732 · outbound

This paper cites an unresolved cited work.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Unresolved cited work

Reference 1112

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Observation ebbaa1ab-b646-43a3-8c9f-8a9b3cec8949 · outbound

This paper cites an unresolved cited work.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Unresolved cited work

Reference 1151

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