World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
51
Citations
35044
World Ranking
3733
National Ranking
1081

Overview

Paris Perdikaris is affiliated with the University of Pennsylvania in the United States. Their research spans multiple disciplines, primarily focusing on engineering, physics and astronomy, and computer science. Within these fields, they have contributed extensively to subfields such as statistical and nonlinear physics, artificial intelligence, computational mechanics, cardiology and cardiovascular medicine, and mechanics of materials.

The scientist's scholarly output demonstrates a concentration on topics related to model reduction and neural networks, neural networks and applications, Gaussian processes and Bayesian inference, fluid dynamics and turbulent flows, probabilistic and robust engineering design, cardiac electrophysiology and arrhythmias, and lattice Boltzmann simulation studies.

Paris Perdikaris has published frequently in a variety of venues, with notable concentrations in:

  • arXiv (Cornell University)
  • Computer Methods in Applied Mechanics and Engineering
  • Journal of Computational Physics
  • Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • Institutional Research Information System (Università degli Studi di Trento)

Coauthors who have collaborated regularly with Paris Perdikaris include:

  • Sifan Wang
  • Francisco Sahli Costabal
  • Simone Pezzuto
  • Hanwen Wang
  • Shyam Sankaran

Selected recent papers of Paris Perdikaris feature topics in physics-informed machine learning and neural networks applied to scientific problems. These include:

  • "Physics-informed machine learning," 2021, published in Nature Reviews Physics
  • "Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks," 2021, SIAM Journal on Scientific Computing
  • "Physics-Informed Neural Networks for Heat Transfer Problems," 2021, Journal of Heat Transfer
  • "When and why PINNs fail to train: A neural tangent kernel perspective," 2021, Journal of Computational Physics
  • "Learning the solution operator of parametric partial differential equations with physics-informed DeepONets," 2021, Science Advances

Best Publications

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

    Maziar Raissi;Paris Perdikaris;George E. Karniadakis

  • Physics-informed machine learning

    George Em Karniadakis;Ioannis G. Kevrekidis;Lu Lu;Paris Perdikaris

  • Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

    Sifan Wang;Yujun Teng;Paris Perdikaris

  • Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

    Maziar Raissi;Paris Perdikaris;George Em Karniadakis

  • When and why PINNs fail to train: A neural tangent kernel perspective

    Sifan Wang;Xinling Yu;Paris Perdikaris

  • Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

    Yinhao Zhu;Nicholas Zabaras;Phaedon-Stelios Koutsourelakis;Paris Perdikaris

  • Physics-Informed Neural Networks for Heat Transfer Problems

    Shengze Cai;Zhicheng Wang;Sifan Wang;Paris Perdikaris

  • Learning the solution operator of parametric partial differential equations with physics-informed DeepONets.

    Sifan Wang;Hanwen Wang;Paris Perdikaris

  • Machine learning of linear differential equations using Gaussian processes

    Maziar Raissi;Paris Perdikaris;George Em Karniadakis

  • Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

    Georgios Kissas;Yibo Yang;Eileen Hwuang;Walter R. Witschey

  • Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

    Mark Alber;Adrian Buganza Tepole;William R. Cannon;Suvranu De

  • On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

    Sifan Wang;Hanwen Wang;Paris Perdikaris

  • Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

    A. M. Tartakovsky;C. Ortiz Marrero;Paris Perdikaris;G. D. Tartakovsky

  • Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling.

    P. Perdikaris;M. Raissi;A. Damianou;N. D. Lawrence

  • Adversarial uncertainty quantification in physics-informed neural networks

    Yibo Yang;Paris Perdikaris

  • Physics-informed neural networks for cardiac activation mapping

    Francisco Sahli Costabal;Yibo Yang;Paris Perdikaris;Daniel E. Hurtado

  • Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations

    Maziar Raissi;Paris Perdikaris;George Em Karniadakis

  • Inferring solutions of differential equations using noisy multi-fidelity data

    Maziar Raissi;Paris Perdikaris;George Em Karniadakis

  • Multiscale modeling meets machine learning: What can we learn?

    Grace C Y Peng;Mark Alber;Adrian Buganza Tepole;William R Cannon

  • Understanding and mitigating gradient pathologies in physics-informed neural networks

    Sifan Wang;Yujun Teng;Paris Perdikaris

  • Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

    Maziar Raissi;Paris Perdikaris;George Em Karniadakis

Frequent Co-Authors

George Em Karniadakis
George Em Karniadakis Brown University
Ellen Kuhl
Ellen Kuhl Stanford University
Alexandre M. Tartakovsky
Alexandre M. Tartakovsky University of Illinois at Urbana-Champaign
William W. Lytton
William W. Lytton SUNY Downstate Medical Center
Suvranu De
Suvranu De Rensselaer Polytechnic Institute
John A. Detre
John A. Detre University of Pennsylvania
Linda R. Petzold
Linda R. Petzold University of California, Santa Barbara
Thomas Pock
Thomas Pock Graz University of Technology
Gernot Plank
Gernot Plank Medical University of Graz
John C. Crocker
John C. Crocker University of Pennsylvania

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