Ameya D. Jagtap

Physics-informed neural networks for high-speed flows

Zhiping Mao, Ameya D. Jagtap, George Em Karniadakis · Computer Methods in Applied Mechanics and Engineering · 2019 · 1.1K citations

Unsteady Flow, Engineering, Physic Aware Machine Learning +4

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

Ameya D. Jagtap, Ehsan Kharazmi, George Em Karniadakis · Computer Methods in Applied Mechanics and Engineering · 2020 · 989 citations · Full text

Numerical Analysis, Engineering, Pde-constrained Optimization +8

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

Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis · Journal of Computational Physics · 2019 · 911 citations · Full text

Evolving Neural Network, Engineering, Machine Learning +11

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 Em Karniadakis · Communications in Computational Physics · 2020 · 864 citations · Full text

Physics-informed neural networks for inverse problems in supersonic\n flows

Ameya D. Jagtap, Zhiping Mao, Nikolaus A. Adams et al. · arXiv (Cornell University) · 2022 · 290 citations · Full text