Ameya Jagtap

Assistant Professor of Applied Mathematics (Research)

Brown University

United States

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About

Ameya Jagtap is an Assistant Professor of Applied Mathematics at Brown University, USA. His research focuses on the application of advanced mathematical techniques and deep learning methods to solve complex problems in physics, particularly those involving partial differential equations (PDEs), shock fronts, and chemical kinetics. Notable recent articles include work on physics-informed neural networks and innovative optimization methods.

Articles (18)

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2025

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2020

Collaborators (5)

Ben Moseley

Lecturer

Imperial College London

UNITED KINGDOM

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Zheyuan Hu

National University of Singapore

SINGAPORE

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Dimitrios Mitsotakis

Victoria University of Wellington

NEW ZEALAND

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Somdatta Goswami

Assistant Professor (Research)

Brown University

UNITED STATES

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Matthias Ehrhardt

Full Professor

Bergische Universität Wuppertal

GERMANY

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Ameya Jagtap

Research Interests

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Articles (18)

Anant-Net: Breaking the curse of dimensionality with scalable and interpretable neural surrogate for high-dimensional PDEs

Challenges and advancements in modeling shock fronts with physics-informed neural networks: A review and benchmarking study

History-Matching of imbibition flow in fractured porous media Using Physics-Informed Neural Networks (PINNs)

Large language model-based evolutionary optimizer: Reasoning with elitism

RiemannONets: Interpretable neural operators for Riemann problems

Deep smoothness weighted essentially non-oscillatory method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators

Learning stiff chemical kinetics using extended deep neural operators

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations

Parallel physics-informed neural networks via domain decomposition

On spatio-temporal dynamics of sine-Gordon soliton in nonlinear non-homogeneous media using fully implicit spectral element scheme

Kinetic theory based multi-level adaptive finite difference WENO schemes for compressible Euler equations

Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

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

L1-type smoothness indicators based WENO scheme for nonlinear degenerate parabolic equations

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

Collaborators (5)

Ben Moseley

Zheyuan Hu

Dimitrios Mitsotakis

Somdatta Goswami

Matthias Ehrhardt

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