© Pallas Group, UCB

Rethinking Physics Informed Neural Networks

Presented by Amir Gholami,

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In collaboration with:

A. Krishnapriyan, S. Subramanian, S. Zhe, M. Kirby, M. Mahoney, K. Keutzer

University of California Berkeley, University of Utah

Babuška Forum Seminar, UT Austin, Oct 2021

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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M.Raissia, P.Perdikarisb,∗, G.E.Karniadakisa

aDivision of Applied Mathematics, Brown University, Providence, RI, 02912, USA

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bDepartment of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, 19104, USA

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Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data

Lena Podina * 1 Brydon Eastman * 2 Mohammad Kohandel

CS598: Physics-Informed Neural Networks:

A deep learning framework for solving forward and inverse

problems involving nonlinear PDEs

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M. Raissi, P. Perdikaris, GE. Karniadakis

Bhavesh Shrimali

November 18, 2021

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FACULTY OF SCIENCE AND TECHNOLOGY

MASTER THESIS

Study programme / specialisation:

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Applied Data Science

The spring semester, 2022

Open / Confidential

Author:

Lars Belbo Lukerstuen

…………………………………………

(signature author)

Course coordinator:

Supervisor(s):

Aksel Hiorth

Thesis title:

A Study on Physics Informed Neural Networks,

with Applications for Compartment Models

Credits (ECTS):

30

Keywords:

PiNN, Machine Learning,

Numerical Computation,

Physics Informed Learning

Pages: 77

+ appendix: 104

Stavanger, 13/06/2022

date/year

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