Paris Perdikaris

发表

Bayesian differential programming for robust systems identification under uncertainty

Yibo Yang, Mohamed Aziz Bhouri, Paris Perdikaris, 2020, Proceedings of the Royal Society A.

Multi-Fidelity Optimization of High Speed SWATHs

Paris Perdikaris, Luca Bonfiglio, Stefano Brizzolara, 2016 .

Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations pdf

Paris Perdikaris, George E. Karniadakis, Maziar Raissi, 2017, ArXiv.

A probabilistic framework for multidisciplinary design: Application to the hydrostructural optimization of supercavitating hydrofoils

George Em Karniadakis, Paris Perdikaris, Luca Bonfiglio, 2018, International Journal for Numerical Methods in Engineering.

Physics-informed deep generative models pdf

Yibo Yang, Paris Perdikaris, P. Perdikaris, 2018, ArXiv.

Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets

Daniele Venturi, Paris Perdikaris, George E. Karniadakis, 2016, SIAM J. Sci. Comput..

A new paradigm for variable-fidelity stochastic simulation and information fusion in fluid mechanics

George Em Karniadakis, Paris Perdikaris, Daniele Venturi, 2015 .

Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization

Wei Cai, Paris Perdikaris, Guofei Pang, 2017, J. Comput. Phys..

Multi-fidelity Bayesian Optimization of SWATH Hull Forms

Paris Perdikaris, Luca Bonfiglio, Stefano Brizzolara, 2020, Journal of Ship Research.

A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations pdf

Paris Perdikaris, Ramakrishna Tipireddy, Alexandre M. Tartakovsky, 2019, ArXiv.

Bayesian differential programming for robust systems identification under uncertainty pdf

Yibo Yang, Paris Perdikaris, Mohamed Aziz Bhouri, 2020, Proceedings of the Royal Society A.

Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems

Yibo Yang, Paris Perdikaris, P. Perdikaris, 2019, Computational Mechanics.

Learning Unknown Physics of non-Newtonian Fluids pdf

Paris Perdikaris, Alexandre M. Tartakovsky, Brandon Reyes, 2020, ArXiv.

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Paris Perdikaris, William W. Lytton, Linda Petzold, 2019 .

Machine Learning of Space-Fractional Differential Equations pdf

Paris Perdikaris, George E. Karniadakis, Maziar Raissi, 2018, SIAM J. Sci. Comput..

An Effective Fractal-Tree Closure Model for Simulating Blood Flow in Large Arterial Networks

Paris Perdikaris, Leopold Grinberg, George Em. Karniadakis, 2014, Annals of Biomedical Engineering.

Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models pdf

Paris Perdikaris, Ellen Kuhl, Francisco Sahli Costabal, 2019, Computer Methods in Applied Mechanics and Engineering.

Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms

Michael E. Papka, Paris Perdikaris, Yue Yu, 2016, Parallel Comput..

Improving SWATH Seakeeping Performance using Multi-Fidelity Gaussian Process and Bayesian Optimization

George Em Karniadakis, Paris Perdikaris, Giuliano Vernengo, 2018, Journal of Ship Research.

Inferring solutions of differential equations using noisy multi-fidelity data

Paris Perdikaris, George E. Karniadakis, Maziar Raissi, 2016, J. Comput. Phys..

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

Paris Perdikaris, Sifan Wang, Yujun Teng, 2020, ArXiv.

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

Hanwen Wang, Paris Perdikaris, Sifan Wang, 2020, ArXiv.

Multi-fidelity optimization of super-cavitating hydrofoils

George Em Karniadakis, Paris Perdikaris, Stefano Brizzolara, 2018 .

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

Paris Perdikaris, George E. Karniadakis, Maziar Raissi, 2017, SIAM J. Sci. Comput..

A multi-fidelity framework for investigating the performance of super-cavitating hydrofoils under uncertain flow conditions

George Em Karniadakis, Paris Perdikaris, Luca Bonfiglio, 2017 .

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data pdf

Paris Perdikaris, Phaedon-Stelios Koutsourelakis, Nicholas Zabaras, 2019, J. Comput. Phys..

Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms

Paris Perdikaris, Yue Yu, George E. Karniadakis, 2016, J. Comput. Phys..

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

Paris Perdikaris, Sifan Wang, Xinling Yu, 2020, J. Comput. Phys..

Machine learning of linear differential equations using Gaussian processes

Paris Perdikaris, Maziar Raissi, George Em Karniadakis, 2017, J. Comput. Phys..

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

Paris Perdikaris, George E. Karniadakis, Maziar Raissi, 2017, ArXiv.

Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration

Paris Perdikaris, Soumalya Sarkar, Shaunak D. Bopardikar, 2019, Journal of Mechanical Design.

Deep learning of free boundary and Stefan problems pdf

Paris Perdikaris, Sifan Wang, P. Perdikaris, 2020, Journal of Computational Physics.

Multi-fidelity Gaussian process regression for prediction of random fields

Daniele Venturi, Paris Perdikaris, George E. Karniadakis, 2017, J. Comput. Phys..

Exact artificial boundary conditions of 1D semi-discretized peridynamics pdf

Jiwei Zhang, Paris Perdikaris, Songsong Ji, 2020, ArXiv.

Calibration of Blood Flow in Simulations via Multi-fidelity Bayesian Optimization

George Em Karniadakis, Paris Perdikaris, 2015 .

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

Paris Perdikaris, George E. Karniadakis, Maziar Raissi, 2019, J. Comput. Phys..

Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning pdf

John A. Detre, Paris Perdikaris, Georgios Kissas, 2019, Computer Methods in Applied Mechanics and Engineering.

Multi-Fidelity Bayesian Optimization of SWATH Vessels for Improving Seakeeping Performance

George Em Karniadakis, Paris Perdikaris, Giuliano Vernengo, 2017 .

Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

Paris Perdikaris, Yibo Yang, P. Perdikaris, 2018, J. Comput. Phys..

Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification pdf

Paris Perdikaris, Simone Pezzuto, Ali Gharaviri, 2021, Frontiers in Physiology.

Physics-Guided AI for Large-Scale Spatiotemporal Data

Anuj Karpatne, Paris Perdikaris, Rose Yu, 2021, KDD.

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

Paris Perdikaris, Sifan Wang, Hanwen Wang, 2021, Science advances.

Adaptive behavior and different thermal experiences of real people: A Bayesian neural network approach to thermal preference prediction and classification

Paris Perdikaris, William W. Braham, Jian Hu, 2021, Building and Environment.

Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs pdf

Yibo Yang, Paris Perdikaris, Antoine Blanchard, 2021, ArXiv.

Long-time integration of parametric evolution equations with physics-informed DeepONets pdf

Paris Perdikaris, Sifan Wang, P. Perdikaris, 2021, J. Comput. Phys..

Learning Unknown Physics of non-Newtonian Fluids pdf

Paris Perdikaris, Alexandre M. Tartakovsky, Amanda A. Howard, 2020, Physical Review Fluids.

Improved Architectures and Training Algorithms for Deep Operator Networks pdf

Paris Perdikaris, Hanwen Wang, Sifan Wang, 2021, Journal of Scientific Computing.

Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks

Paris Perdikaris, Gernot Plank, Thomas Pock, 2021, FIMH.

Fast PDE-constrained optimization via self-supervised operator learning pdf

Paris Perdikaris, Mohamed Aziz Bhouri, Sifan Wang, 2021, ArXiv.

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

Paris Perdikaris, Mohamed Aziz Bhouri, Ellen Kuhl, 2021 .

Deep learning of free boundary and Stefan problems

Paris Perdikaris, Sifan Wang, P. Perdikaris, 2020, J. Comput. Phys..

Gaussian processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data pdf

Paris Perdikaris, Mohamed Aziz Bhouri, P. Perdikaris, 2021, Philosophical Transactions of the Royal Society A.

Learning Operators with Coupled Attention pdf

George J. Pappas, Paris Perdikaris, Victor M. Preciado, 2022, J. Mach. Learn. Res..

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

Paris Perdikaris, Carlos Ortiz Marrero, P. Perdikaris, 2020, Water Resources Research.

Physics-Informed Neural Networks for Cardiac Activation Mapping

Yibo Yang, Paris Perdikaris, Ellen Kuhl, 2020, Frontiers in Physics.

Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

Paris Perdikaris, Alexandre M. Tartakovsky, David Barajas-Solano, 2018, 1808.03398.

Gaussian processes A hands-on tutorial

Houman Owhadi, Paris Perdikaris, P. Perdikaris, 2017 .

Inferring solutions of dierential equations using noisy multi-delity data

George Em Karniadakis, Paris Perdikaris, Maziar Raissi, 2016 .