Ameya D. Jagtap

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 25

Top Topics

Inverse Problems

Top Venues

J. Comput. Phys.

Eng. Appl. Artif. Intell.

Appl. Math. Comput.

Publications

Shuvayan Brahmachary, Subodh M. Joshi, Aniruddha Panda, Kaushik Koneripalli, Arun Kumar Sagotra, Harshil Patel, Ankush Sharma, Ameya D. Jagtap, Kaushic Kalyanaraman
Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism. CoRR (2024)
Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap, George Em Karniadakis
RiemannONets: Interpretable Neural Operators for Riemann Problems. CoRR (2024)
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. Eng. Appl. Artif. Intell. 126 (2023)
Tatiana Kossaczká, Ameya D. Jagtap, Matthias Ehrhardt
Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators. CoRR (2023)
Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. CoRR (2023)
Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee, Bryan T. Susi, George Em Karniadakis
Learning stiff chemical kinetics using extended deep neural operators. CoRR (2023)
Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. J. Comput. Phys. 493 (2023)
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? SIAM J. Sci. Comput. 44 (5) (2022)
Ameya D. Jagtap, George Em Karniadakis
How important are activation functions in regression and classification? A survey, performance comparison, and future directions. CoRR (2022)
Ameya D. Jagtap, Dimitrios Mitsotakis, George Em Karniadakis
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations. CoRR (2022)
Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams, George Em Karniadakis
Physics-informed neural networks for inverse problems in supersonic flows. J. Comput. Phys. 466 (2022)
Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams, George Em Karniadakis
Physics-informed neural networks for inverse problems in supersonic flows. CoRR (2022)
Tim De Ryck, Ameya D. Jagtap, Siddhartha Mishra
Error estimates for physics informed neural networks approximating the Navier-Stokes equations. CoRR (2022)
Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. Neurocomputing 468 (2022)
Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman, George Em Karniadakis
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. 39 (1) (2022)
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. CoRR (2022)
Ameya D. Jagtap, George E. Karniadakis
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 (2021)
Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
Parallel Physics-Informed Neural Networks via Domain Decomposition. CoRR (2021)
Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
Parallel physics-informed neural networks via domain decomposition. J. Comput. Phys. 447 (2021)
Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. CoRR (2021)
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? CoRR (2021)
Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. J. Comput. Phys. 404 (2020)
Samala Rathan, Rakesh Kumar, Ameya D. Jagtap
-type smoothness indicators based WENO scheme for nonlinear degenerate parabolic equations. Appl. Math. Comput. 375 (2020)
Ameya D. Jagtap, Kenji Kawaguchi, George E. Karniadakis
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks. CoRR (2019)
Ameya D. Jagtap, A. S. Vasudeva Murthy
Higher order scheme for two-dimensional inhomogeneous sine-Gordon equation with impulsive forcing. Commun. Nonlinear Sci. Numer. Simul. 64 (2018)