Paris Perdikaris

99 papers A* 9Journal 88Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Multimodal Scientific Learning Beyond Diffusions and Flows. CoRR Leonardo Ferreira Guilhoto, Akshat Kaushal, Paris Perdikaris
2026	A*	`conf`	PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations. AAAI Xinquan Huang, Paris Perdikaris
2026	J	`jnl`	Self-Flow-Matching assisted Full Waveform Inversion. CoRR Xinquan Huang, Paris Perdikaris
2025	J	`jnl`	A foundation model for the Earth system. Nat. Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Jayesh K. Gupta, Kit Thambiratnam, Alexander T. Archibald, Chun-Chieh Wu, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
2025	J	`jnl`	Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators. CoRR Gregory M. Campbell, Gentian Muhaxheri, Leonardo Ferreira Guilhoto, Christian D. Santangelo, Paris Perdikaris, James H. Pikul, Mark Yim
2025	J	`jnl`	CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators. CoRR Xianglong Hou, Xinquan Huang, Paris Perdikaris
2025	A*	`conf`	CViT: Continuous Vision Transformer for Operator Learning. ICLR Sifan Wang, Jacob H. Seidman, Shyam Sankaran, Hanwen Wang, George J. Pappas, Paris Perdikaris
2025	A*	`conf`	Deep Learning Alternatives Of The Kolmogorov Superposition Theorem. ICLR Leonardo Ferreira Guilhoto, Paris Perdikaris
2025	J	`jnl`	Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective. CoRR Sifan Wang, Ananyae Kumar Bhartari, Bowen Li, Paris Perdikaris
2025	J	`jnl`	Multi-fidelity Bayesian optimization of shear-wall building costs. Eng. Comput. Felipe Concha, Paris Perdikaris, Francisco Sahli Costabal
2025	J	`jnl`	Neural Networks as Surrogate Solvers for Time-Dependent Accretion Disk Dynamics. CoRR Shunyuan Mao, Weiqi Wang, Sifan Wang, Ruobing Dong, Lu Lu, Kwang Moo Yi, Paris Perdikaris, Andrea Isella, Sébastien Fabbro, Lile Wang
2025	J	`jnl`	PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations. CoRR Xinquan Huang, Paris Perdikaris
2025	J	`jnl`	Simulating Three-dimensional Turbulence with Physics-informed Neural Networks. CoRR Sifan Wang, Shyam Sankaran, Panos Stinis, Paris Perdikaris
2024	J	`jnl`	Aurora: A Foundation Model of the Atmosphere. CoRR Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Thambiratnam, Alexander T. Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
2024	J	`jnl`	Bridging Operator Learning and Conditioned Neural Fields: A Unifying Perspective. CoRR Sifan Wang, Jacob H. Seidman, Shyam Sankaran, Hanwen Wang, George J. Pappas, Paris Perdikaris
2024	J	`jnl`	Composite Bayesian Optimization In Function Spaces Using NEON - Neural Epistemic Operator Networks. CoRR Leonardo Ferreira Guilhoto, Paris Perdikaris
2024	J	`jnl`	Deep Learning Alternatives of the Kolmogorov Superposition Theorem. CoRR Leonardo Ferreira Guilhoto, Paris Perdikaris
2024	J	`jnl`	Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems. CoRR Shunyuan Mao, Ruobing Dong, Kwang Moo Yi, Lu Lu, Sifan Wang, Paris Perdikaris
2024	J	`jnl`	Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries. Neural Comput. Zhiwei Fang, Sifan Wang, Paris Perdikaris
2024	J	`jnl`	Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials. CoRR Sifan Wang, Tong-Rui Liu, Shyam Sankaran, Paris Perdikaris
2024	A*	`conf`	On conditional diffusion models for PDE simulations. NeurIPS Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu
2024	J	`jnl`	On conditional diffusion models for PDE simulations. CoRR Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu
2024	J	`jnl`	Physics-Informed Neural Networks and Extensions. CoRR Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi Daryakenari, George Em Karniadakis
2024	J	`jnl`	PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks. CoRR Sifan Wang, Bowen Li, Yuhan Chen, Paris Perdikaris
2024	J	`jnl`	PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks. J. Mach. Learn. Res. Sifan Wang, Bowen Li, Yuhan Chen, Paris Perdikaris
2024	J	`jnl`	Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions. CoRR Xinyu Liao, Aoyang Qin, Jacob H. Seidman, Junqi Wang, Wei Wang, Paris Perdikaris
2024	J	`jnl`	Δ-PINNs: Physics-informed neural networks on complex geometries. Eng. Appl. Artif. Intell. Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
2023	J	`jnl`	An Expert's Guide to Training Physics-informed Neural Networks. CoRR Sifan Wang, Shyam Sankaran, Hanwen Wang, Paris Perdikaris
2023	J	`jnl`	Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach. CoRR Zhiwei Fang, Sifan Wang, Paris Perdikaris
2023	J	`jnl`	Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior. CoRR Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas
2023	J	`jnl`	Learning Only On Boundaries: a Physics-Informed Neural operator for Solving Parametric Partial Differential Equations in Complex Geometries. CoRR Zhiwei Fang, Sifan Wang, Paris Perdikaris
2023	J	`jnl`	Long-time integration of parametric evolution equations with physics-informed DeepONets. J. Comput. Phys. Sifan Wang, Paris Perdikaris
2023	A*	`conf`	Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. ICML Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne
2023	A*	`conf`	PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. NeurIPS Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
2023	J	`jnl`	PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. CoRR Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
2023	J	`jnl`	PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems. CoRR Shunyuan Mao, Ruobing Dong, Lu Lu, Kwang Moo Yi, Sifan Wang, Paris Perdikaris
2023	J	`jnl`	Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks. CoRR Mohamed Aziz Bhouri, Michael Joly, Robert Yu, Soumalya Sarkar, Paris Perdikaris
2023	A*	`conf`	Variational Autoencoding Neural Operators. ICML Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris
2023	J	`jnl`	Variational Autoencoding Neural Operators. CoRR Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris
2022	—	`conf`	Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior. CDC Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas
2022	J	`jnl`	Improved Architectures and Training Algorithms for Deep Operator Networks. J. Sci. Comput. Sifan Wang, Hanwen Wang, Paris Perdikaris
2022	J	`jnl`	Learning Operators with Coupled Attention. CoRR Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris
2022	J	`jnl`	Learning Operators with Coupled Attention. J. Mach. Learn. Res. Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris
2022	J	`jnl`	Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds. CoRR Simone Pezzuto, Paris Perdikaris, Francisco Sahli Costabal
2022	A*	`conf`	NOMAD: Nonlinear Manifold Decoders for Operator Learning. NeurIPS Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas
2022	J	`jnl`	NOMAD: Nonlinear Manifold Decoders for Operator Learning. CoRR Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas
2022	J	`jnl`	Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps. CoRR Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto
2022	J	`jnl`	Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps. Eng. Comput. Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto
2022	J	`jnl`	Random Weight Factorization Improves the Training of Continuous Neural Representations. CoRR Sifan Wang, Hanwen Wang, Jacob H. Seidman, Paris Perdikaris
2022	J	`jnl`	Respecting causality is all you need for training physics-informed neural networks. CoRR Sifan Wang, Shyam Sankaran, Paris Perdikaris
2022	J	`jnl`	Rethinking the Importance of Sampling in Physics-informed Neural Networks. CoRR Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne
2022	J	`jnl`	Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors. CoRR Yibo Yang, Georgios Kissas, Paris Perdikaris
2022	J	`jnl`	Semi-supervised Invertible DeepONets for Bayesian Inverse Problems. CoRR Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis
2022	J	`jnl`	When and why PINNs fail to train: A neural tangent kernel perspective. J. Comput. Phys. Sifan Wang, Xinling Yu, Paris Perdikaris
2022	J	`jnl`	Δ-PINNs: physics-informed neural networks on complex geometries. CoRR Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
2021	J	`jnl`	Deep learning of free boundary and Stefan problems. J. Comput. Phys. Sifan Wang, Paris Perdikaris
2021	J	`jnl`	Fast PDE-constrained optimization via self-supervised operator learning. CoRR Sifan Wang, Mohamed Aziz Bhouri, Paris Perdikaris
2021	J	`jnl`	Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification. CoRR Lia Gander, Simone Pezzuto, Ali Gharaviri, Rolf Krause, Paris Perdikaris, Francisco Sahli Costabal
2021	J	`jnl`	Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data. CoRR Mohamed Aziz Bhouri, Paris Perdikaris
2021	J	`jnl`	Improved architectures and training algorithms for deep operator networks. CoRR Sifan Wang, Hanwen Wang, Paris Perdikaris
2021	—	`conf`	Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks. FIMH Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause
2021	J	`jnl`	Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks. CoRR Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause
2021	J	`jnl`	Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets. CoRR Sifan Wang, Hanwen Wang, Paris Perdikaris
2021	J	`jnl`	Long-time integration of parametric evolution equations with physics-informed DeepONets. CoRR Sifan Wang, Paris Perdikaris
2021	J	`jnl`	Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs. CoRR Yibo Yang, Antoine Blanchard, Themistoklis P. Sapsis, Paris Perdikaris
2021	A*	`conf`	Physics-Guided AI for Large-Scale Spatiotemporal Data. KDD Rose Yu, Paris Perdikaris, Anuj Karpatne
2021	J	`jnl`	Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks. SIAM J. Sci. Comput. Sifan Wang, Yujun Teng, Paris Perdikaris
2020	J	`jnl`	Bayesian differential programming for robust systems identification under uncertainty. CoRR Yibo Yang, Mohamed Aziz Bhouri, Paris Perdikaris
2020	J	`jnl`	Deep learning of free boundary and Stefan problems. CoRR Sifan Wang, Paris Perdikaris
2020	J	`jnl`	Exact artificial boundary conditions of 1D semi-discretized peridynamics. CoRR Songsong Ji, Gang Pang, Jiwei Zhang, Yibo Yang, Paris Perdikaris
2020	J	`jnl`	Learning Unknown Physics of non-Newtonian Fluids. CoRR Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky
2020	J	`jnl`	On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks. CoRR Sifan Wang, Hanwen Wang, Paris Perdikaris
2020	J	`jnl`	Understanding and mitigating gradient pathologies in physics-informed neural networks. CoRR Sifan Wang, Yujun Teng, Paris Perdikaris
2020	J	`jnl`	When and why PINNs fail to train: A neural tangent kernel perspective. CoRR Sifan Wang, Xinling Yu, Paris Perdikaris
2019	J	`jnl`	A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. CoRR Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky
2019	J	`jnl`	Adversarial uncertainty quantification in physics-informed neural networks. J. Comput. Phys. Yibo Yang, Paris Perdikaris
2019	J	`jnl`	Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems. CoRR Yibo Yang, Paris Perdikaris
2019	J	`jnl`	Integrating machine learning and multiscale modeling - perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digit. Medicine Mark S. Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna C. Garikipati, George E. Karniadakis, William W. Lytton, Paris Perdikaris, Linda R. Petzold, Ellen Kuhl
2019	J	`jnl`	Machine Learning of Space-Fractional Differential Equations. SIAM J. Sci. Comput. Mamikon A. Gulian, Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2019	J	`jnl`	Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning. CoRR Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter R. Witschey, John A. Detre, Paris Perdikaris
2019	J	`jnl`	Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models. CoRR Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl, Daniel E. Hurtado
2019	J	`jnl`	Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data. CoRR Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
2019	J	`jnl`	Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
2019	J	`jnl`	Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2018	J	`jnl`	Adversarial Uncertainty Quantification in Physics-Informed Neural Networks. CoRR Yibo Yang, Paris Perdikaris
2018	J	`jnl`	Machine Learning of Space-Fractional Differential Equations. CoRR Mamikon A. Gulian, Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2018	J	`jnl`	Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations. SIAM J. Sci. Comput. Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2018	J	`jnl`	Physics-informed deep generative models. CoRR Yibo Yang, Paris Perdikaris
2017	J	`jnl`	Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization. J. Comput. Phys. Guofei Pang, Paris Perdikaris, Wei Cai, George E. Karniadakis
2017	J	`jnl`	Inferring solutions of differential equations using noisy multi-fidelity data. J. Comput. Phys. Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2017	J	`jnl`	Machine learning of linear differential equations using Gaussian processes. J. Comput. Phys. Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2017	J	`jnl`	Multi-fidelity Gaussian process regression for prediction of random fields. J. Comput. Phys. Lucia Parussini, Daniele Venturi, Paris Perdikaris, George E. Karniadakis
2017	J	`jnl`	Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations. CoRR Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2017	J	`jnl`	Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations. CoRR Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2017	J	`jnl`	Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations. CoRR Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2016	J	`jnl`	Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms. J. Comput. Phys. Yue Yu, Paris Perdikaris, George E. Karniadakis
2016	J	`jnl`	Inferring solutions of differential equations using noisy multi-fidelity data. CoRR Maziar Raissi, Paris Perdikaris, George E. Karniadakis
2016	J	`jnl`	Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets. SIAM J. Sci. Comput. Paris Perdikaris, Daniele Venturi, George E. Karniadakis
2016	J	`jnl`	Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms. Parallel Comput. Paris Perdikaris, Joseph A. Insley, Leopold Grinberg, Yue Yu, Michael E. Papka, George E. Karniadakis