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Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations.

A. Jagtap, and G. Karniadakis. AAAI Spring Symposium: MLPS, volume 2964 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)

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Sunil Jagtap

Smita Jagtap

Avinash Baburao Jagtap

D Breunig

D Bruß

Other publications of authors with the same name

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, A. Jagtap, J. Blackshire, D. Sparkman, and G. Karniadakis. IEEE Signal Process. Mag., 39 (1): 68-77 (2022)Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations.A. Jagtap, D. Mitsotakis, and G. Karniadakis. CoRR, (2022)Error estimates for physics informed neural networks approximating the Navier-Stokes equations.T. Ryck, A. Jagtap, and S. Mishra. CoRR, (2022)Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks.A. Jagtap, K. Kawaguchi, and G. Karniadakis. CoRR, (2019)Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism.S. Brahmachary, S. Joshi, A. Panda, K. Koneripalli, A. Sagotra, H. Patel, A. Sharma, A. Jagtap, and K. Kalyanaraman. CoRR, (2024)A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions.M. Penwarden, A. Jagtap, S. Zhe, G. Karniadakis, and R. Kirby. CoRR, (2023)Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology.Z. Hu, A. Jagtap, G. Karniadakis, and K. Kawaguchi. Eng. Appl. Artif. Intell., (November 2023)Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators.T. Kossaczká, A. Jagtap, and M. Ehrhardt. CoRR, (2023)Physics-informed neural networks for inverse problems in supersonic flows.A. Jagtap, Z. Mao, N. Adams, and G. Karniadakis. CoRR, (2022)RiemannONets: Interpretable Neural Operators for Riemann Problems.A. Peyvan, V. Oommen, A. Jagtap, and G. Karniadakis. CoRR, (2024)

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Disambiguation of "Jagtap, Ameya D."