Paris Perdikaris

35.6k total citations · 17 hit papers
79 papers, 20.4k citations indexed

About

Paris Perdikaris is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Statistics, Probability and Uncertainty. According to data from OpenAlex, Paris Perdikaris has authored 79 papers receiving a total of 20.4k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Statistical and Nonlinear Physics, 25 papers in Artificial Intelligence and 16 papers in Statistics, Probability and Uncertainty. Recurrent topics in Paris Perdikaris's work include Model Reduction and Neural Networks (35 papers), Gaussian Processes and Bayesian Inference (17 papers) and Probabilistic and Robust Engineering Design (16 papers). Paris Perdikaris is often cited by papers focused on Model Reduction and Neural Networks (35 papers), Gaussian Processes and Bayesian Inference (17 papers) and Probabilistic and Robust Engineering Design (16 papers). Paris Perdikaris collaborates with scholars based in United States, Chile and Switzerland. Paris Perdikaris's co-authors include George Em Karniadakis, Maziar Raissi, Sifan Wang, Lu Lu, Liu Yang, Ioannis G. Kevrekidis, Hanwen Wang, Yibo Yang, Xinling Yu and Phaedon‐Stelios Koutsourelakis and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and The Astrophysical Journal.

In The Last Decade

Paris Perdikaris

72 papers receiving 19.7k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2018 8.7k citations Maziar Raissi, Paris Perdikaris et al. Journal of Computational Physics profile →
Physics-informed machine learning

2021 3.8k citations George Em Karniadakis, Ioannis G. Kevrekidis et al. Nature Reviews Physics profile →
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

2021 859 citations Sifan Wang, Paris Perdikaris et al. profile →
Physics-Informed Neural Networks for Heat Transfer Problems

2021 686 citations Sifan Wang, Paris Perdikaris et al. profile →
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

2019 672 citations Yinhao Zhu, Nicholas Zabaras et al. Journal of Computational Physics profile →
When and why PINNs fail to train: A neural tangent kernel perspective

2021 672 citations Sifan Wang, Paris Perdikaris et al. Journal of Computational Physics profile →
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

2021 439 citations Sifan Wang, Hanwen Wang et al. profile →
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

2019 405 citations Mark Alber, Adrián Buganza Tepole et al. npj Digital Medicine profile →
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

2019 394 citations Georgios Kissas, Yibo Yang et al. Computer Methods in Applied Mechanics and Engineering profile →
Machine learning of linear differential equations using Gaussian processes

2017 376 citations Maziar Raissi, Paris Perdikaris et al. Journal of Computational Physics profile →
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

2021 355 citations Sifan Wang, Hanwen Wang et al. Computer Methods in Applied Mechanics and Engineering profile →
Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

2020 318 citations Alexandre M. Tartakovsky, Carlos Ortiz Marrero et al. Water Resources Research profile →
Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling

2017 279 citations Paris Perdikaris, Maziar Raissi et al. profile →
Adversarial uncertainty quantification in physics-informed neural networks

2019 277 citations Yibo Yang, Paris Perdikaris Journal of Computational Physics profile →
Physics-Informed Neural Networks for Cardiac Activation Mapping

2020 263 citations Francisco Sahli Costabal, Yibo Yang et al. profile →
Respecting causality for training physics-informed neural networks

2024 120 citations Sifan Wang, Paris Perdikaris et al. Computer Methods in Applied Mechanics and Engineering profile →
A foundation model for the Earth system

2025 24 citations Paris Perdikaris et al. profile →

Peers

Countries citing papers authored by Paris Perdikaris

Since Specialization

Citations

This map shows the geographic impact of Paris Perdikaris's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Paris Perdikaris with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paris Perdikaris more than expected).

Fields of papers citing papers by Paris Perdikaris

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paris Perdikaris. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Paris Perdikaris. The network helps show where Paris Perdikaris may publish in the future.

Co-authorship network of co-authors of Paris Perdikaris

This figure shows the co-authorship network connecting the top 25 collaborators of Paris Perdikaris. A scholar is included among the top collaborators of Paris Perdikaris based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Paris Perdikaris. Paris Perdikaris is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators 2025 ·IEEE Robotics and Automation Letters ·(unknown), (unknown), (unknown), Christian D. Santangelo, Paris Perdikaris, James H. Pikul, Mark Yim	0
2	Pulmonary Artery Shear Stress and Oscillatory Shear Index are Associated with Right Ventricular Remodeling in Repaired Tetralogy of Fallot 2025 ·Annals of Biomedical Engineering ·Elizabeth Thompson, Amal Bhattacharya, Fengling Hu, Russell T. Shinohara, Paris Perdikaris, Kevin K. Whitehead, Elizabeth Goldmuntz, Mark A. Fogel, Walter R. Witschey	0
3	Multi-fidelity Bayesian optimization of shear-wall building costs 2025 ·Engineering With Computers ·F. Concha, Paris Perdikaris, Francisco Sahli Costabal	0
4	Composite Bayesian Optimization in function spaces using NEON—Neural Epistemic Operator Networks 2024 ·Scientific Reports ·(unknown), Paris Perdikaris	3
5	Scalable Bayesian optimization with randomized prior networks 2023 ·Computer Methods in Applied Mechanics and Engineering ·(unknown), (unknown), Robert K. Yu, Soumalya Sarkar, Paris Perdikaris	6
6	Δ-PINNs: Physics-informed neural networks on complex geometries 2023 ·Engineering Applications of Artificial Intelligence ·Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris	24
7	Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps 2022 ·Engineering With Computers ·(unknown), Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto	1
8	Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps 2022 ·Institutional Research Information System (Università degli Studi di Trento) ·(unknown), Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto	28
9	COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior 2021 ·Computer Methods in Applied Mechanics and Engineering ·(unknown), Francisco Sahli Costabal, Hanwen Wang, Kevin Linka, Mathias Peirlinck, Ellen Kuhl, Paris Perdikaris	36
10	Learning unknown physics of non-Newtonian fluids 2021 ·Physical Review Fluids ·(unknown), (unknown), Paris Perdikaris, Alexandre M. Tartakovsky	50
11	Physics-informed machine learning breakdown → 2021 ·Nature Reviews Physics ·George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, Liu Yang	3772
12	Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems breakdown → 2020 ·Water Resources Research ·Alexandre M. Tartakovsky, Carlos Ortiz Marrero, Paris Perdikaris, G. Tartakovsky, David A. Barajas‐Solano	318
13	Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models 2019 ·Computer Methods in Applied Mechanics and Engineering ·Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl, Daniel E. Hurtado	44
14	Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data breakdown → 2019 ·Journal of Computational Physics ·Yinhao Zhu, Nicholas Zabaras, Phaedon‐Stelios Koutsourelakis, Paris Perdikaris	672
15	Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning 2019 ·arXiv (Cornell University) ·Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter R. Witschey, John A. Detre, Paris Perdikaris	3
16	Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences breakdown → 2019 ·npj Digital Medicine ·Mark Alber, Adrián Buganza Tepole, William R. Cannon, Suvranu De, Salvador Durá-Bernal, Krishna Garikipati, George Em Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold, Ellen Kuhl	405
17	A probabilistic framework for multidisciplinary design: Application to the hydrostructural optimization of supercavitating hydrofoils 2018 ·International Journal for Numerical Methods in Engineering ·Luca Bonfiglio, Paris Perdikaris, (unknown), George Em Karniadakis	9
18	Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations breakdown → 2018 ·Journal of Computational Physics ·Maziar Raissi, Paris Perdikaris, George Em Karniadakis	8742
19	Multi-fidelity optimization of super-cavitating hydrofoils 2017 ·Computer Methods in Applied Mechanics and Engineering ·Luca Bonfiglio, Paris Perdikaris, Stefano Brizzolara, George Em Karniadakis	46
20	Multi-Fidelity Optimization of High Speed SWATHs 2016 ·SNAME Maritime Convention ·Luca Bonfiglio, Stefano Brizzolara, Paris Perdikaris	5

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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