Ameya Jagtap
Email
ajagtap@wpi.edu
Office
Higgins Lab, Room No: HL 249
ORCiD
https://orcid.org/0000-0002-8831-1000
Personal Website
Affiliated Department or Office
Aerospace Engineering Department
Education
Ph.D. in Aerospace Engineering, Indian Institute of Science, India (2016)
M.E. in Aerospace Engineering, Indian Institute of Science, India (2010)

Prior to joining WPI, I served as an Assistant Professor of Applied Mathematics (Research) at Brown University for three and a half years. My academic journey includes earning both my PhD and Master's degrees in Aerospace Engineering from the esteemed Indian Institute of Science (IISc) in India. Following this, I engaged in postdoctoral research at the Tata Institute of Fundamental Research—Center for Applicable Mathematics (TIFR-CAM) in India. Subsequently, I transitioned to Brown University to continue my postdoctoral research within the Division of Applied Mathematics. My research is uniquely positioned at the intersection of mechanical/aerospace engineering, applied mathematics, and computation. I am particularly dedicated to advancing scientific machine learning algorithms that seamlessly integrate data and physics, offering versatile applications across computational physics. My areas of expertise encompass scientific machine learning, deep learning, data- and physics-driven deep learning techniques, uncertainty quantification, and propagation, as well as multi-scale/multi-physics simulations (solids, fluids, and acoustics). I bring proficiency in spectral/finite element methods, WENO/DG schemes, and domain decomposition techniques, among others. Beyond these, I am actively engaged in more traditional machine learning algorithms such as deep generative models, and novel artificial neural network architectures, such as quantum and graph neural networks. To this end, my interests also extend to spiking neural networks and other bio-inspired computing techniques. I also serve on the editorial board of several prestigious journals, including Neurocomputing and Neural Networks, both published by Elsevier.

Ameya Jagtap
Email
ajagtap@wpi.edu
Affiliated Department or Office
Aerospace Engineering Department
Education
Ph.D. in Aerospace Engineering, Indian Institute of Science, India (2016)
M.E. in Aerospace Engineering, Indian Institute of Science, India (2010)

Prior to joining WPI, I served as an Assistant Professor of Applied Mathematics (Research) at Brown University for three and a half years. My academic journey includes earning both my PhD and Master's degrees in Aerospace Engineering from the esteemed Indian Institute of Science (IISc) in India. Following this, I engaged in postdoctoral research at the Tata Institute of Fundamental Research—Center for Applicable Mathematics (TIFR-CAM) in India. Subsequently, I transitioned to Brown University to continue my postdoctoral research within the Division of Applied Mathematics. My research is uniquely positioned at the intersection of mechanical/aerospace engineering, applied mathematics, and computation. I am particularly dedicated to advancing scientific machine learning algorithms that seamlessly integrate data and physics, offering versatile applications across computational physics. My areas of expertise encompass scientific machine learning, deep learning, data- and physics-driven deep learning techniques, uncertainty quantification, and propagation, as well as multi-scale/multi-physics simulations (solids, fluids, and acoustics). I bring proficiency in spectral/finite element methods, WENO/DG schemes, and domain decomposition techniques, among others. Beyond these, I am actively engaged in more traditional machine learning algorithms such as deep generative models, and novel artificial neural network architectures, such as quantum and graph neural networks. To this end, my interests also extend to spiking neural networks and other bio-inspired computing techniques. I also serve on the editorial board of several prestigious journals, including Neurocomputing and Neural Networks, both published by Elsevier.

Office
Higgins Lab, Room No: HL 249
ORCiD
https://orcid.org/0000-0002-8831-1000
Personal Website

Scholarly Work

  1. A. Peyvan, V. Oommen, Ameya D. Jagtap, G. E. Karniadakis,  RiemannONets: Interpretable Neural Operators for Riemann Problems,Computer Methods in Applied Mechanics and Engineering, Volume 426, 116996 (2024) [Journal] [arXiv] 

  2. T. Kossaczka, Ameya D. Jagtap, Matthias Ehrhardt, Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators, Physics of Fluids 36, 036603 (2024) [Journal] 

  3. S. Goswami, Ameya D. Jagtap, H. Babaee, B. T. Susi, and G.E. Karniadakis, Learning stiff chemical kinetics using extended deep neural operators, Computer Methods in Applied Mechanics and Engineering, Volume 419 (2024) 116674. [Journal] [arXiv]

  4. Z. Hu, Ameya D. Jagtap, G. E. Karniadakis, K. Kawaguchi,  Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. Engineering Applications of Artificial Intelligence, Volume 126  (2023) 107183.  [Journal] [arXiv]

  5. M. Penwarden, Ameya D. Jagtap, S. Zhe, G.E.Karniadakis, M. Kirby, A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions, Journal of Computational Physics, Volume 493 (2023) 112464[Journal] [arXiv]

  6. Ameya D. Jagtap, G.E. Karniadakis, How important are activation functions for regression and classification? A survey, performance comparison, and future directions, Journal of Machine Learning for Modeling and Computing, Volume 4, Issue 1, 2023, pp. 21-75  [Journal][arXiv]

  7. T. De Ryck, Ameya D.  Jagtap, and S. Mishra, Error estimates for physics informed neural networks approximating the Navier-Stokes equations, IMA Journal of Numerical Analysis, Oxford Academic, 2023.  https://doi.org/10.1093/imanum/drac085 [Journal] [arXiv]

  8. Ameya D. Jagtap, Z. Mao, N. Adams, G.E. Karniadakis, Physics-informed neural networks for inverse problems in supersonic flows,  Journal of Computational Physics, 466 (2022) 111402. [Journal] [arXiv]

  9. Z. Hu, Ameya D. Jagtap, G. E. Karniadakis, K. Kawaguchi,  When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?,  SIAM Journal on Scientific Computing, Vol. 44, No. 5, pp. A3158–A3182. [Journal] [arXiv]

  10. Ameya D. Jagtap, D. Mitsotakis, G. E. Karniadakis, Deep learning of inverse water waves problems using multi-fidelity data:  Application to Serre-Green-Naghdi equations, Ocean Engineering, 248 (2022) 110775.  [Journal] [arXiv]

  11. Ameya D. Jagtap, Y. Shin, K. Kawaguchi, G. E. Karniadakis, Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions, Neurocomputing, Vol. 468, (2022), 165-180. [Journal] [arXiv] 

  12. K. Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman, and G. E. Karniadakis, A physics-informed neural network for quantifying the microstructure properties of polycrystalline Nickel using ultrasound data, IEEE Signal Processing Magazine, vol. 39, no. 1, pp. 68-77, Jan. 2022.  [Journal] [arXiv]

  13. K. Shukla, Ameya D. Jagtap, G. E. Karniadakis, Parallel Physics-Informed Neural Networks via Domain Decomposition, Journal of Computational Physics, 447 (2021) 110683. [Journal] [arXiv]

  14. Ameya D. Jagtap, G. E. Karniadakis, Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations, Communications in Computational Physics, Vol. 28, No. 5, pp. 2002-2041, 2020. [Journal]

  15. Ameya D. Jagtap, R. Kumar, Kinetic theory based multi-level finite difference WENO schemes for compressible Euler equations, Wave Motion, Vol. 98, November 2020, 102626. [Journal]

  16. Ameya D. Jagtap, K. Kawaguchi, G. E. Karniadakis, Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476: 20200334, 2020. [Journal] [arXiv]

  17. Ameya D. Jagtap, E. Kharazmi, G. E. Karniadakis, Conservative Physics-Informed Neural Networks on Discrete Domains for Conservation Laws: Applications to forward and inverse problems, Computer Methods in Applied Mechanics and Engineering, 365 (2020) 113028.[Journal]

  18. S. Rathan, R. Kumar, Ameya D. Jagtap, L1-type smoothness indicators based WENO scheme for nonlinear degenerate parabolic equations, Applied Mathematics and Computation, 375 (2020) 125112. [Journal]

  19. Ameya D. Jagtap, K. Kawaguchi, G. E. Karniadakis,  Adaptive activation functions accelerate convergence in deep and physics-informed neural networks, Journal of Computational Physics, 404 (2020) 109136. [Journal][arXiv]

  20. Z. Mao,  Ameya D. Jagtap, G. E. Karniadakis, Physics-Informed Neural Networks for high-speed flows, Computer Methods in Applied Mechanics and Engineering, 360 (2020) 112789. [Journal]

  21. Ameya D. Jagtap, On spatio-temporal dynamics of sine-Gordon soliton in nonlinear non-homogeneous media using fully implicit spectral element scheme, Applicable Analysis, 100 : 1, 37-60, 2021. DOI: 10.1080/00036811.2019.1588961  [Journal]

  22. Ameya D. Jagtap, A. S. V. Murthy, Higher order spectral element scheme for two- and three-dimensional Cahn-Hilliard equation, International Journal of Advances in Engineering Sciences and Applied Mathematics, Vol. 10, Issue 1, 79 - 89, 2018. [Journal]

  23. Ameya D. Jagtap, A. S. V. Murthy, Higher order scheme for two-dimensional inhomogeneous Sine-Gordon equation with impulsive forcing, Communications in Nonlinear Science and Numerical Simulation, 64, 178-197, 2018.  [Journal]

  24. Ameya D. Jagtap, Method of relaxed streamlined-upwinding for hyperbolic conservation laws, Wave Motion, 78, 132-161, 2018. [Journal] [arXiv]

  25. M Saravanan, Ameya D. Jagtap,  A. S. V. Murthy, Perturbed soliton and director deformation in a ferronematic liquid crystal, Chaos, Solitons & Fractals, 106, 220-226, 2018. [Journal]

  26. Ameya D. Jagtap, E. Saha, J.D. George, A. S. V. Murthy, Revisiting the inhomogeneously driven Sine-Gordon equation, Wave Motion, 73, 76-85, 2017. [Journal]