AJ
Ameya D. Jagtap
cs.LGcs.NAmath.NAphysics.flu-dynmath.MPmath-phphysics.comp-phcs.CEmath.DSstat.ML
On Valency
published · living versionsW_6rxhnvx4·v1 · currentpublished
Preprints & journals
32 papers in the corpus · 2016–2026Glass-Box Deep Reinforcement Learning for High-Speed Flow Control2610.27132v1 · Ameya D. Jagtap2026 · 0 citationsarXivon Valency
Physics-Informed Deep Learning in Oncology2610.18682v1 · Scindhiya Laxmi, Vivek S. Yadav, Ameya D. Jagtap2026 · 0 citationsarXiv
The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning2609.33078v2 · Ameya D. Jagtap2026 · 0 citationsarXiv
GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model2609.23761v1 · Trishit Mondal, Prajwal Bharadwaj, Nikhil Karanjgaokar et al.2026 · 0 citationsarXiv
Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients2609.14841v2 · Sidharth S. Menon, Irina Tezaur, Ameya D. Jagtap2026 · 0 citationsarXiv
ENPINN: Energy-Norm-Guided Gradient-Enhanced PINNs for Generalized Transport Problems with Sharp Gradients2608.29499v1 · Subhendu Maity, Pratibhamoy Das, Arihant Patawari et al.2026 · 0 citationsarXiv
MARUT: An Exascale-Ready, GPU-Accelerated High-Order CFD Framework with AMR for High-Speed Flows and Finite-Rate Chemistry2605.26388v4 · Trishit Mondal, Ameya D. Jagtap2026 · 0 citationsarXiv
An approximation theory perspective on machine learning.41855885 · Mhaskar, Hrushikesh N, Tsoukanis, Efstratios, Jagtap, Ameya D2026 · 4 citationsNeural networks : the official journal of the International Neural Network Society. 2026;200:108841
FEKAN: Feature-Enriched Kolmogorov-Arnold Networks2602.16530v1 · Sidharth S. Menon, Ameya D. Jagtap2026 · 0 citationsarXiv
In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes2602.14318v1 · Trishit Mondal, Ameya D. Jagtap2026 · 0 citationsarXiv
Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty2602.02531v1 · Trishit Mondal, Ameya D. Jagtap2026 · 1 citationarXiv
On scientific foundation models: Rigorous definitions, key applications, and a comprehensive survey.41547123 · Menon, Sidharth S, Mondal, Trishit, Brahmachary, Shuvayan et al.2026 · 11 citationsNeural networks : the official journal of the International Neural Network Society. 2026;198:108567
Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning2511.07564v1 · Trishit Mondal, Ricardo Vinuesa, Ameya D. Jagtap2025 · 0 citationsarXiv
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs2505.03595v3 · Sidharth S. Menon, Ameya D. Jagtap2025 · 13 citationsarXiv
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study2503.17379v1 · Jassem Abbasi, Ameya D. Jagtap, Ben Moseley et al.2025 · 46 citationsarXiv
History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs)2410.20801v2 · Jassem Abbasi, Ben Moseley, Takeshi Kurotori et al.2024 · 1 citationarXiv
Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism2403.02054v1 · Shuvayan Brahmachary, Subodh M. Joshi, Aniruddha Panda et al.2024 · 46 citationsarXiv
RiemannONets: Interpretable Neural Operators for Riemann Problems2401.08886v2 · Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap et al.2024 · 40 citationsarXiv
Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators2309.10117v1 · Tatiana Kossaczk'a, Ameya D. Jagtap, Matthias Ehrhardt2023 · 1 citationarXiv
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions2302.14227v2 · Michael Penwarden, Ameya D. Jagtap, Shandian Zhe et al.2023 · 126 citationsJournal of Computational Physics, 493, 2023, 112464
Learning stiff chemical kinetics using extended deep neural operators2302.12645v1 · Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee et al.2023 · 74 citationsarXiv
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology2211.08939v3 · Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis et al.2022 · 146 citationsEngineering Applications of Artificial Intelligence, Volume 126, Part B, November 2023, 107183
How important are activation functions in regression and classification? A survey, performance comparison, and future directions2209.02681v6 · Ameya D. Jagtap, George Em Karniadakis2022 · 131 citationsarXiv
Error estimates for physics informed neural networks approximating the Navier-Stokes equations2203.09346v2 · Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra2022 · 149 citationsarXiv
Physics-informed neural networks for inverse problems in supersonic flows2202.11821v1 · Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams et al.2022 · 356 citationsarXiv
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations2202.02899v1 · Ameya D. Jagtap, Dimitrios Mitsotakis, George Em Karniadakis2022 · 97 citationsarXiv
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?2109.09444v7 · Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis et al.2021 · 141 citationsSIAM Journal on Scientific Computing Vol. 44, Iss. 5 (2022)
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions2105.09513v2 · Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi et al.2021 · 188 citationsarXiv
Parallel Physics-Informed Neural Networks via Domain Decomposition2104.10013v3 · Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis2021 · 436 citationsarXiv
A physics-informed neural network for quantifying the microstructure properties of polycrystalline Nickel using ultrasound data2103.14104v2 · Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire et al.2021 · 125 citationsarXiv
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks1906.01170v1 · Ameya D. Jagtap, George Em Karniadakis2019 · 1,136 citationsarXiv
Method of Relaxed Streamline Upwinding for Hyperbolic Conservation Laws1611.03338v4 · Ameya D. Jagtap2016 · 2 citationsarXiv
Career total: 49 works. 32 are in this corpus.
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