Ameya D. Jagtap

Orcid: 0000-0002-8831-1000

According to our database1, Ameya D. Jagtap authored at least 19 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism.
CoRR, 2024

RiemannONets: Interpretable Neural Operators for Riemann Problems.
CoRR, 2024

2023
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions.
J. Comput. Phys., November, 2023

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology.
Eng. Appl. Artif. Intell., November, 2023

Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators.
CoRR, 2023

Learning stiff chemical kinetics using extended deep neural operators.
CoRR, 2023

2022
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., 2022

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?
SIAM J. Sci. Comput., 2022

Physics-informed neural networks for inverse problems in supersonic flows.
J. Comput. Phys., 2022

Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions.
Neurocomputing, 2022

How important are activation functions in regression and classification? A survey, performance comparison, and future directions.
CoRR, 2022

Error estimates for physics informed neural networks approximating the Navier-Stokes equations.
CoRR, 2022

Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations.
CoRR, 2022

2021
Parallel physics-informed neural networks via domain decomposition.
J. Comput. Phys., 2021

Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

2020
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks.
J. Comput. Phys., 2020

<i>L</i><sup>1</sup>-type smoothness indicators based WENO scheme for nonlinear degenerate parabolic equations.
Appl. Math. Comput., 2020

2019
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks.
CoRR, 2019

2018
Higher order scheme for two-dimensional inhomogeneous sine-Gordon equation with impulsive forcing.
Commun. Nonlinear Sci. Numer. Simul., 2018


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