Higher order scheme for two-dimensional inhomogeneous sine-Gordon equation with impulsive forcing. - Ameya D. Jagtap; A. S. Vasudeva Murthy (Commun. Nonlinear Sci. Numer. Simul., 2018)

Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks. - Ameya D. Jagtap; Kenji Kawaguchi; George E. Karniadakis (CoRR, 2019)

L1-type smoothness indicators based WENO scheme for nonlinear degenerate parabolic equations. - Samala Rathan; Rakesh Kumar 006; Ameya D. Jagtap (Appl. Math. Comput., 2020)

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. - Ameya D. Jagtap; Kenji Kawaguchi; George Em Karniadakis (J. Comput. Phys., 2020)

Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations. - Ameya D. Jagtap; George E. Karniadakis (AAAI Spring Symposium: MLPS, 2021)

Parallel physics-informed neural networks via domain decomposition. - Khemraj Shukla; Ameya D. Jagtap; George Em Karniadakis (J. Comput. Phys., 2021)

Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations. - Ameya D. Jagtap; Dimitrios Mitsotakis; George Em Karniadakis (CoRR, 2022)

Error estimates for physics informed neural networks approximating the Navier-Stokes equations. - Tim De Ryck; Ameya D. Jagtap; Siddhartha Mishra (CoRR, 2022)

How important are activation functions in regression and classification? A survey, performance comparison, and future directions. - Ameya D. Jagtap; George Em Karniadakis (CoRR, 2022)

Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. - Ameya D. Jagtap; Yeonjong Shin; Kenji Kawaguchi; George Em Karniadakis (Neurocomputing, 2022)

Physics-informed neural networks for inverse problems in supersonic flows. - Ameya D. Jagtap; Zhiping Mao; Nikolaus A. Adams; George Em Karniadakis (J. Comput. Phys., 2022)

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? - Zheyuan Hu 002; Ameya D. Jagtap; George Em Karniadakis; Kenji Kawaguchi (SIAM J. Sci. Comput., 2022)

A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems. - Khemraj Shukla; Ameya D. Jagtap; James L. Blackshire; Daniel Sparkman; George Em Karniadakis (IEEE Signal Process. Mag., 2022)

Learning stiff chemical kinetics using extended deep neural operators. - Somdatta Goswami; Ameya D. Jagtap; Hessam Babaee; Bryan T. Susi; George Em Karniadakis (CoRR, 2023)

Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators. - Tatiana Kossaczká; Ameya D. Jagtap; Matthias Ehrhardt 001 (CoRR, 2023)

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. - Zheyuan Hu 002; Ameya D. Jagtap; George Em Karniadakis; Kenji Kawaguchi (Eng. Appl. Artif. Intell., 2023)

A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. - Michael Penwarden; Ameya D. Jagtap; Shandian Zhe; George Em Karniadakis; Robert M. Kirby (J. Comput. Phys., 2023)

RiemannONets: Interpretable Neural Operators for Riemann Problems. - Ahmad Peyvan; Vivek Oommen; Ameya D. Jagtap; George Em Karniadakis (CoRR, 2024)

History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs). - Jassem Abbasi; Ben Moseley; Takeshi Kurotori; Ameya D. Jagtap; Anthony R. Kovscek; Aksel Hiorth; Pål Østebø Andersen (CoRR, 2024)

Large language model-based evolutionary optimizer: Reasoning with elitism. - Shuvayan Brahmachary; Subodh M. Joshi; Aniruddha Panda; Kaushik Koneripalli; Arun Kumar Sagotra; Harshil Patel; Ankush Sharma; Ameya D. Jagtap; Kaushic Kalyanaraman (Neurocomputing, 2025)

Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study. - Jassem Abbasi; Ameya D. Jagtap; Ben Moseley; Aksel Hiorth; Pål Østebø Andersen (CoRR, 2025)

Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs. - Sidharth S. Menon; Ameya D. Jagtap (CoRR, 2025)

An Approximation Theory Perspective on Machine Learning. - Hrushikesh N. Mhaskar; Efstratios Tsoukanis; Ameya D. Jagtap (CoRR, 2025)

BubbleONet: A Physics-Informed Neural Operator for High-Frequency Bubble Dynamics. - Yunhao Zhang; Lin Cheng; Aswin Gnanaskandan; Ameya D. Jagtap (CoRR, 2025)