Paris Perdikaris
Orcid: 0000-0002-2816-3229Affiliations:
- University of Pennsylvania, Department of Mechanical Engineering and Applied Mechanics, Philadelphia, USA
According to our database1,
Paris Perdikaris
authored at least 67 papers
between 2016 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
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Online presence:
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on zbmath.org
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on orcid.org
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Bibliography
2024
Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries.
Neural Comput., March, 2024
Eng. Appl. Artif. Intell., January, 2024
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions.
CoRR, 2024
Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials.
CoRR, 2024
Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems.
CoRR, 2024
CoRR, 2024
Composite Bayesian Optimization In Function Spaces Using NEON - Neural Epistemic Operator Networks.
CoRR, 2024
CoRR, 2024
2023
Long-time integration of parametric evolution equations with physics-informed DeepONets.
J. Comput. Phys., February, 2023
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems.
CoRR, 2023
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach.
CoRR, 2023
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the International Conference on Machine Learning, 2023
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling.
Proceedings of the International Conference on Machine Learning, 2023
2022
J. Sci. Comput., 2022
J. Comput. Phys., 2022
Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps.
Eng. Comput., 2022
Random Weight Factorization Improves the Training of Continuous Neural Representations.
CoRR, 2022
CoRR, 2022
CoRR, 2022
Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds.
CoRR, 2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors.
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the 61st IEEE Conference on Decision and Control, 2022
2021
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks.
SIAM J. Sci. Comput., 2021
Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification.
CoRR, 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets.
CoRR, 2021
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data.
CoRR, 2021
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021
Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks.
Proceedings of the Functional Imaging and Modeling of the Heart, 2021
2020
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks.
CoRR, 2020
Bayesian differential programming for robust systems identification under uncertainty.
CoRR, 2020
Understanding and mitigating gradient pathologies in physics-informed neural networks.
CoRR, 2020
2019
SIAM J. Sci. Comput., 2019
Integrating machine learning and multiscale modeling - perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences.
npj Digit. Medicine, 2019
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data.
J. Comput. Phys., 2019
J. Comput. Phys., 2019
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.
J. Comput. Phys., 2019
Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning.
CoRR, 2019
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models.
CoRR, 2019
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations.
CoRR, 2019
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems.
CoRR, 2019
2018
Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations.
SIAM J. Sci. Comput., 2018
2017
J. Comput. Phys., 2017
J. Comput. Phys., 2017
J. Comput. Phys., 2017
Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization.
J. Comput. Phys., 2017
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.
CoRR, 2017
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.
CoRR, 2017
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations.
CoRR, 2017
2016
Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets.
SIAM J. Sci. Comput., 2016
Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms.
Parallel Comput., 2016
J. Comput. Phys., 2016