受强制性开放获取政策约束的文章 - Panos Stinis了解详情
无法在其他位置公开访问的文章：2 篇
Stochastic basis adaptation and spatial domain decomposition for partial differential equations with random coefficients
R Tipireddy, P Stinis, AM Tartakovsky
SIAM/ASA Journal on Uncertainty Quantification 6 (1), 273-301, 2018
强制性开放获取政策： US Department of Energy
Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance
Y Fu, A Howard, C Zeng, Y Chen, P Gao, P Stinis
ACS Energy Letters 9, 2767-2774, 2024
强制性开放获取政策： US Department of Energy
可在其他位置公开访问的文章：34 篇
Multifidelity deep operator networks for data-driven and physics-informed problems
AA Howard, M Perego, GE Karniadakis, P Stinis
Journal of Computational Physics 493, 112462, 2023
强制性开放获取政策： US Department of Energy
Renormalized Mori–Zwanzig-reduced models for systems without scale separation
P Stinis
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015
强制性开放获取政策： US Department of Energy
Multistep and continuous physics-informed neural network methods for learning governing equations and constitutive relations
R Tipireddy, P Perdikaris, P Stinis, AM Tartakovsky
Journal of Machine Learning for Modeling and Computing 3 (2), 2022
强制性开放获取政策： US Department of Energy
Machine learning structure preserving brackets for forecasting irreversible processes
K Lee, N Trask, P Stinis
Advances in Neural Information Processing Systems 34, 5696-5707, 2021
强制性开放获取政策： US Department of Energy
Enforcing constraints for interpolation and extrapolation in generative adversarial networks
P Stinis, T Hagge, AM Tartakovsky, E Yeung
Journal of Computational Physics 397, 108844, 2019
强制性开放获取政策： US Department of Energy
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
QZ He, P Stinis, AM Tartakovsky
Journal of Power Sources 528, 231147, 2022
强制性开放获取政策： US Department of Energy
Machine-learning-based spectral methods for partial differential equations
B Meuris, S Qadeer, P Stinis
Scientific Reports 13 (1), 1739, 2023
强制性开放获取政策： US Department of Energy
Doing the impossible: Why neural networks can be trained at all
NO Hodas, P Stinis
Frontiers in psychology 9, 1185, 2018
强制性开放获取政策： US Department of Energy
Structure-preserving sparse identification of nonlinear dynamics for data-driven modeling
K Lee, N Trask, P Stinis
Mathematical and Scientific Machine Learning, 65-80, 2022
强制性开放获取政策： US Department of Energy
A hybrid deep neural operator/finite element method for ice-sheet modeling
QZ He, M Perego, AA Howard, GE Karniadakis, P Stinis
Journal of Computational Physics 492, 112428, 2023
强制性开放获取政策： US Department of Energy
Renormalized reduced order models with memory for long time prediction
J Price, P Stinis
Multiscale Modeling & Simulation 17 (1), 68-91, 2019
强制性开放获取政策： US Department of Energy
A multifidelity deep operator network approach to closure for multiscale systems
SE Ahmed, P Stinis
Computer Methods in Applied Mechanics and Engineering 414, 116161, 2023
强制性开放获取政策： US Department of Energy
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
QZ He, Y Fu, P Stinis, A Tartakovsky
Journal of Power Sources 542, 231807, 2022
强制性开放获取政策： US Department of Energy
Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients
R Tipireddy, P Stinis, AM Tartakovsky
Journal of Computational Physics 351, 203-215, 2017
强制性开放获取政策： US Department of Energy
Dynamic looping of a free-draining polymer
FXF Ye, P Stinis, H Qian
SIAM Journal on Applied Mathematics 78 (1), 104-123, 2018
强制性开放获取政策： US Department of Energy
A unified framework for mesh refinement in random and physical space
J Li, P Stinis
Journal of Computational Physics 323, 243-264, 2016
强制性开放获取政策： US Department of Energy
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
W Chen, Y Fu, P Stinis
Journal of Power Sources 584, 233548, 2023
强制性开放获取政策： US Department of Energy
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations
W Chen, P Stinis
Journal of Computational Physics 498, 112683, 2024
强制性开放获取政策： US Department of Energy
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