Ameya D. Jagtap - Concepedia

Concepedia

Author

Ameya D. Jagtap

Brown University ORCID

Also Known As

Ameya D. Jagtap, Ameya D Jagtap, Ameya D. Jagtap, Ameya Jagtap, H Singh, Jagtap Ameya D, Jagtap, Ameya D.

56

Publications

5.1K

Citations

16

H-Index

41

Concepts

All Affiliations

Brown University John Brown University National University of Singapore Pacific Northwest National Laboratory Worcester Polytechnic Institute

AD

About

Ameya D. Jagtap is an author at Brown University specializing in engineering, numerical analysis, and machine learning.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	12	14	3.4K
2	Computer Science	3	3	1K
3	Data Science	3	3	202
4	Social Sciences	1	1	99
5	Applied Physics	1	1	10

Ameya D. Jagtap

×

29

Publications

5.1K

Citations

16

H-Index

	Year	Citations
Physics-informed neural networks for high-speed flows Zhiping Mao, Ameya D. Jagtap, George Em Karniadakis Computer Methods in Applied Mechanics and Engineering Unsteady FlowEngineeringPhysic Aware Machine LearningFluid MechanicsFlow Physic	2019	1.1K
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 Numerical AnalysisEngineeringPde-constrained OptimizationPhysicsPhysic Aware Machine Learning	2020	989
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis Journal of Computational Physics Evolving Neural NetworkEngineeringMachine LearningPhysicsDeep Reinforcement Learning	2019	911
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
Physics-informed neural networks for inverse problems in supersonic\n flows Ameya D. Jagtap, Zhiping Mao, Nikolaus A. Adams, arXiv (Cornell University)	2022	290
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Engineering Applications of Artificial Intelligence	2023	124
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, SIAM Journal on Scientific Computing	2022	115
A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, IEEE Signal Processing Magazine	2021	108
HOW IMPORTANT ARE ACTIVATION FUNCTIONS IN REGRESSION AND CLASSIFICATION? A SURVEY, PERFORMANCE COMPARISON, AND FUTURE DIRECTIONS Ameya D. Jagtap, George Em Karniadakis Journal of Machine Learning for Modeling and Computing EngineeringMachine LearningAi FoundationSocial SciencesClassification Method	2023	99
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations Ameya D. Jagtap, Dimitrios Mitsotakis, George Em Karniadakis Ocean Engineering Numerical AnalysisEngineeringOcean EngineeringData SciencePhysic Aware Machine Learning	2022	91

Page 1