Paris Perdikaris
- Statistical and Nonlinear Physics top 0.01%
- Computational Mechanics top 0.05%
- Artificial Intelligence top 0.2%
- Mechanical Engineering top 0.5%
- Aerospace Engineering top 0.2%
- Co-authors
- George Em KarniadakisMaziar RaissiSifan WangLu LuLiu YangIoannis G. KevrekidisHanwen WangYibo Yang
- Topics
- Model Reduction and Neural Networks (35 papers)Gaussian Processes and Bayesian Inference (17 papers)Probabilistic and Robust Engineering Design (16 papers)
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyComputational Mechanics
- Partner nations
- United StatesChileSwitzerland
In The Last Decade
Paris Perdikaris
72 papers receiving 19.7k citations
Hit Papers
Peers
Comparison fields: 5 of 189
- Statistical and Nonlinear Physics 10.6k
- Computational Mechanics 4.5k
- Artificial Intelligence 3.6k
- Mechanical Engineering 2.7k
- Aerospace Engineering 2.5k
Countries citing papers authored by Paris Perdikaris
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 6 | |
| 6 | 24 | |
| 7 | 1 | |
| 8 | 28 | |
| 9 | 36 | |
| 10 | 50 | |
| 11 | Physics-informed machine learningbreakdown → | 3772 |
| 12 | Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problemsbreakdown → | 318 |
| 13 | 44 | |
| 14 | Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled databreakdown → | 672 |
| 15 | Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning | 3 |
| 16 | Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciencesbreakdown → | 405 |
| 17 | 9 | |
| 18 | Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equationsbreakdown → | 8742 |
| 19 | 46 | |
| 20 | Multi-Fidelity Optimization of High Speed SWATHs | 5 |
About Paris Perdikaris
Paris Perdikaris is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty and Modeling and Simulation, having authored 79 papers that have together received 20.4k indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (35 papers), Gaussian Processes and Bayesian Inference (17 papers) and Probabilistic and Robust Engineering Design (16 papers). The work is most often cited by research in Statistical and Nonlinear Physics (10.6k citations), Statistics, Probability and Uncertainty (2.2k citations) and Computational Mechanics (4.5k citations). Paris Perdikaris has collaborated with scholars based in United States, Chile and Switzerland. Frequent 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. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and The Astrophysical Journal.
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.