Ameya D. Jagtap

37 papers Journal 36Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	An approximation theory perspective on machine learning. Neural Networks Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
2026	J	`jnl`	FEKAN: Feature-Enriched Kolmogorov-Arnold Networks. CoRR Sidharth S. Menon, Ameya D. Jagtap
2026	J	`jnl`	Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty. CoRR Trishit Mondal, Ameya D. Jagtap
2026	J	`jnl`	In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes. CoRR Trishit Mondal, Ameya D. Jagtap
2026	J	`jnl`	On scientific foundation models: Rigorous definitions, key applications, and a comprehensive survey. Neural Networks Sidharth S. Menon, Trishit Mondal, Shuvayan Brahmachary, Aniruddha Panda, Subodh M. Joshi, Kaushic Kalyanaraman, Ameya D. Jagtap
2025	J	`jnl`	An Approximation Theory Perspective on Machine Learning. CoRR Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
2025	J	`jnl`	Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs. CoRR Sidharth S. Menon, Ameya D. Jagtap
2025	J	`jnl`	BubbleONet: A Physics-Informed Neural Operator for High-Frequency Bubble Dynamics. CoRR Yunhao Zhang, Lin Cheng, Aswin Gnanaskandan, Ameya D. Jagtap
2025	J	`jnl`	Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study. CoRR Jassem Abbasi, Ameya D. Jagtap, Ben Moseley, Aksel Hiorth, Pål Østebø Andersen
2025	J	`jnl`	Challenges and advancements in modeling shock fronts with physics-informed neural networks: A review and benchmarking study. Neurocomputing Jassem Abbasi, Ameya D. Jagtap, Ben Moseley, Aksel Hiorth, Pål Østebø Andersen
2025	J	`jnl`	Large language model-based evolutionary optimizer: Reasoning with elitism. Neurocomputing Shuvayan Brahmachary, Subodh M. Joshi, Aniruddha Panda, Kaushik Koneripalli, Arun Kumar Sagotra, Harshil Patel, Ankush Sharma, Ameya D. Jagtap, Kaushic Kalyanaraman
2025	J	`jnl`	Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning. CoRR Trishit Mondal, Ricardo Vinuesa, Ameya D. Jagtap
2024	J	`jnl`	History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs). CoRR Jassem Abbasi, Ben Moseley, Takeshi Kurotori, Ameya D. Jagtap, Anthony R. Kovscek, Aksel Hiorth, Pål Østebø Andersen
2024	J	`jnl`	Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism. CoRR Shuvayan Brahmachary, Subodh M. Joshi, Aniruddha Panda, Kaushik Koneripalli, Arun Kumar Sagotra, Harshil Patel, Ankush Sharma, Ameya D. Jagtap, Kaushic Kalyanaraman
2024	J	`jnl`	RiemannONets: Interpretable Neural Operators for Riemann Problems. CoRR Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap, George Em Karniadakis
2023	J	`jnl`	A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. CoRR Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby
2023	J	`jnl`	A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. J. Comput. Phys. Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby
2023	J	`jnl`	Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. Eng. Appl. Artif. Intell. Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
2023	J	`jnl`	Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators. CoRR Tatiana Kossaczká, Ameya D. Jagtap, Matthias Ehrhardt
2023	J	`jnl`	Learning stiff chemical kinetics using extended deep neural operators. CoRR Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee, Bryan T. Susi, George Em Karniadakis
2022	J	`jnl`	A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems. IEEE Signal Process. Mag. Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman, George Em Karniadakis
2022	J	`jnl`	Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. CoRR Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
2022	J	`jnl`	Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. Neurocomputing Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis
2022	J	`jnl`	Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations. CoRR Ameya D. Jagtap, Dimitrios Mitsotakis, George Em Karniadakis
2022	J	`jnl`	Error estimates for physics informed neural networks approximating the Navier-Stokes equations. CoRR Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra
2022	J	`jnl`	How important are activation functions in regression and classification? A survey, performance comparison, and future directions. CoRR Ameya D. Jagtap, George Em Karniadakis
2022	J	`jnl`	Physics-informed neural networks for inverse problems in supersonic flows. CoRR Ameya D. Jagtap, Zhiping Mao, Nikolaus A. Adams, George Em Karniadakis
2022	J	`jnl`	Physics-informed neural networks for inverse problems in supersonic flows. J. Comput. Phys. Ameya D. Jagtap, Zhiping Mao, Nikolaus A. Adams, George Em Karniadakis
2022	J	`jnl`	When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? SIAM J. Sci. Comput. Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
2021	J	`jnl`	Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. CoRR Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis
2021	—	`conf`	Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations. AAAI Spring Symposium: MLPS Ameya D. Jagtap, George E. Karniadakis
2021	J	`jnl`	Parallel Physics-Informed Neural Networks via Domain Decomposition. CoRR Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
2021	J	`jnl`	Parallel physics-informed neural networks via domain decomposition. J. Comput. Phys. Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
2021	J	`jnl`	When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? CoRR Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
2020	J	`jnl`	Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. J. Comput. Phys. Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis
2019	J	`jnl`	Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks. CoRR Ameya D. Jagtap, Kenji Kawaguchi, George E. Karniadakis
2018	J	`jnl`	Higher order scheme for two-dimensional inhomogeneous sine-Gordon equation with impulsive forcing. Commun. Nonlinear Sci. Numer. Simul. Ameya D. Jagtap, A. S. Vasudeva Murthy