Pratanu Roy

Lawrence Livermore National Laboratory

P.O. Box 808

Livermore, CA 94551

United States

  • Lawrence Livermore National Laboratory

SCHOLARLY PAPERS

4

DOWNLOADS

429

TOTAL CITATIONS

2

Scholarly Papers (4)

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1.

Adaptive Interface-PINNs(AdaI-PINNs) for Inverse Problems: Determining Material Properties for Heterogeneous Systems

Number of pages: 28 Posted: 19 Oct 2024 Last Revised: 06 May 2025
Dibakar Roy Sarkar, Chandrasekhar Annavarapu and Pratanu Roy
Indian Institute of Technology (IIT), Madras, Indian Institute of Technology (IIT), Madras and Lawrence Livermore National Laboratory
Downloads 183 (356,547)

Abstract:

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physics-informed neural networks, inverse problems, discontinuous material properties, multi-material diffusion

2.

Interface Pinns: A Framework of Physics-Informed Neural Networks for Interface Problems

Number of pages: 51 Posted: 20 Mar 2024
Antareep Sarma, Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy and Sriram Jagannathan
affiliation not provided to SSRN, Indian Institute of Technology (IIT), Madras, Indian Institute of Technology (IIT), Madras, Lawrence Livermore National Laboratory and affiliation not provided to SSRN
Downloads 121 (502,877)
Citation 2

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Physics-informed Neural Networks, heterogeneous materials, Deep Learning, partial differential equations, Physics-informed machine learning

3.

Adaptive Interface-PINNs (AdaI-PINNs) for Transient Diffusion: Applications to Forward and Inverse Problems in Heterogeneous Media

Number of pages: 36 Posted: 28 Sep 2024 Last Revised: 13 Feb 2025
Sumanta Roy, Dibakar Roy Sarkar, Chandrasekhar Annavarapu, Pratanu Roy, Brice Lecampion and Dakshina Valiveti
Indian Institute of Technology (IIT), Madras, Indian Institute of Technology (IIT), Madras, Indian Institute of Technology (IIT), Madras, Lawrence Livermore National Laboratory, affiliation not provided to SSRN and ExxonMobil Technology and Engineering Company
Downloads 90 (619,975)

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physics-informed neural networks, Deep learning, heterogeneous media, Interface Problems, transient diffusion, discontinuities

4.

Scaled-Up Prediction of Steady Navier-Stokes Equation with Component Reduced Order Modeling

Number of pages: 35 Posted: 18 Nov 2024
SeungWhan Chung, Youngsoo Choi, Pratanu Roy, Thomas Roy, Tiras Y. Lin, Du T. Nguyen, Christopher Hahn, Eric B. Duoss and Sarah E. Baker
University of Illinois at Urbana-Champaign, Lawrence Livermore National Laboratory, Lawrence Livermore National Laboratory, Lawrence Livermore National Laboratory, Lawrence Livermore National Laboratory, Lawrence Livermore National Laboratory, Lawrence Livermore National Laboratory, Lawrence Livermore National Laboratory and Lawrence Livermore National Laboratory
Downloads 35 (988,303)

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Reduced order modeling, Steady incompressible Navier-Stokes flow, Discontinuous Galerkin domain decomposition, Empirical quadrature procedure, Supremizer enrichment