Articles with public access mandates - Ameya D. JagtapLearn more
Not available anywhere: 2
L1 - type smoothness indicators based WENO scheme for nonlinear degenerate parabolic equations
S Rathan, R Kumar, AD Jagtap
Applied Mathematics and Computation 375, 125112, 2020
Mandates: Department of Science & Technology, India
Kinetic theory based multi-level adaptive finite difference WENO schemes for compressible Euler equations
AD Jagtap, R Kumar
Wave Motion, 2020
Mandates: Department of Science & Technology, India
Available somewhere: 13
Physics-informed neural networks for high-speed flows
Z Mao, AD Jagtap, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 360, 112789, 2020
Mandates: US Department of Defense
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
AD Jagtap, E Kharazmi, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 365, 113028, 2020
Mandates: US Department of Energy, US Department of Defense
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
AD Jagtap, K Kawaguchi, GE Karniadakis
Journal of Computational Physics 404, 109136, 2020
Mandates: US Department of Energy, US Department of Defense
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
AD Jagtap, GE Karniadakis
Communications in Computational Physics 28 (5), 2002-2041, 2020
Mandates: US Department of Energy, US Department of Defense
Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
AD Jagtap, K Kawaguchi, GE Karniadakis
Proceedings of the Royal Society A 476 (2239), 20200334, 2020
Mandates: US Department of Energy, US Department of Defense
Parallel physics-informed neural networks via domain decomposition
K Shukla, AD Jagtap, GE Karniadakis
Journal of Computational Physics 447, 110683, 2021
Mandates: US Department of Energy, US Department of Defense
Physics-informed neural networks for inverse problems in supersonic flows
AD Jagtap, Z Mao, N Adams, GE Karniadakis
Journal of Computational Physics 466, 111402, 2022
Mandates: US Department of Defense
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions
AD Jagtap, Y Shin, K Kawaguchi, GE Karniadakis
Neurocomputing 468, 165-180, 2022
Mandates: US Department of Energy, US Department of Defense
A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems
K Shukla, AD Jagtap, JL Blackshire, D Sparkman, GE Karniadakis
IEEE Signal Processing Magazine 39 (1), 68-77, 2022
Mandates: US Department of Energy, US Department of Defense
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
Z Hu, AD Jagtap, GE Karniadakis, K Kawaguch
Engineering Applications of Artificial Intelligence 126, 107183, 2023
Mandates: US Department of Energy, US Department of Defense
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
M Penwarden, AD Jagtap, S Zhe, GE Karniadakis, RM Kirby
Journal of Computational Physics 493 (112464), 2023
Mandates: US Department of Defense
RiemannONets: Interpretable Neural Operators for Riemann Problems
A Peyvan, V Oommen, AD Jagtap, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 426 (116996), 2024
Mandates: US Department of Defense
History-Matching of imbibition flow in fractured porous media Using Physics-Informed Neural Networks (PINNs)
J Abbasi, B Moseley, T Kurotori, AD Jagtap, AR Kovscek, A Hiorth, ...
Computer Methods in Applied Mechanics and Engineering 437 (117784), 2025
Mandates: Research Council of Norway
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