Leaderboards/Model Reduction and Neural Networks

Model Reduction and Neural Networks

Last updated July 7, 2026

Rising Stars

New and hot

Rising Stars Researchers

1
Lvmin Zhang
Stanford University
2
Florinel-Alin Croitoru
University of Bucharest
3
Laura von Rueden
Fraunhofer Institute for Intelligent Analysis and Information Systems
4
Hamidreza Eivazi
Clausthal University of Technology
5
Jeremy Yu
St. Mark's School of Texas
6
Ron Mokady
Tel Aviv University
7
Moritz Flaschel
ETH Zurich
8
Levi D. McClenny
Oak Ridge Associated Universities
9
Zheyuan Hu
National University of Singapore
10
Zixue Xiang
National University of Defense Technology

Rising Stars Papers

1
Tackling the curse of dimensionality with physics-informed neural networks
Neural Networks|2024
152 cit.
2
Physics-informed deep-learning applications to experimental fluid mechanics
Measurement Science and Technology|2024
60 cit.
3
Convex neural networks learn generalized standard material models
Journal of the Mechanics and Physics of Solids|2025
34 cit.
4
Hutchinson Trace Estimation for high-dimensional and high-order Physics-Informed Neural Networks
Computer Methods in Applied Mechanics and Engineering|2024
33 cit.
5
Transparent Image Layer Diffusion using Latent Transparency
ACM Transactions on Graphics|2024
29 cit.
6
Solving spatiotemporal partial differential equations with Physics-informed Graph Neural Network
Applied Soft Computing|2024
25 cit.
7
Learning Rate Curriculum
International Journal of Computer Vision|2024
21 cit.
8
Two-stage attention network for fault diagnosis and retrieval of fault logs
Expert Systems with Applications|2024
17 cit.
9
Physics-informed Neural Implicit Flow neural network for parametric PDEs
Neural Networks|2025
11 cit.
10
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations
SIAM Journal on Scientific Computing|2025
11 cit.

Emerging Leaders

Gaining momentum

Emerging Leaders Researchers

1
Paris Perdikaris
University of Pennsylvania
2
Vincenzo Schiano Di Cola
University of Naples Federico II
3
Maziar Raissi
Brown University
4
Fabio Giampaolo
University of Naples Federico II
5
Bing Yu
Xi'an Institute of Optics and Precision Mechanics
6
Romit Maulik
Pennsylvania State University
7
Jannis Schuecker
Fraunhofer Institute for Machine Tools and Forming Technology
8
Nikolaos Bouklas
Cornell University
9
Roberto Molinaro
ETH Zurich
10
Amir Barati Farimani
Carnegie Mellon University

Emerging Leaders Papers

1
Molecular contrastive learning of representations via graph neural networks
Nature Machine Intelligence|2022
799 cit.
2
Respecting causality for training physics-informed neural networks
Computer Methods in Applied Mechanics and Engineering|2024
269 cit.
3
TransPolymer: a Transformer-based language model for polymer property predictions
npj Computational Materials|2023
187 cit.
4
A physics-informed diffusion model for high-fidelity flow field reconstruction
Journal of Computational Physics|2023
180 cit.
5
MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction
Journal of the American Chemical Society|2023
156 cit.
6
MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning
Additive manufacturing|2022
139 cit.
7
A foundation model for the Earth system
Nature|2025
132 cit.
8
Adaptive grey wolf optimizer
Neural Computing and Applications|2022
124 cit.
9
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Computer Methods in Applied Mechanics and Engineering|2022
119 cit.
10
Improved Architectures and Training Algorithms for Deep Operator Networks
Journal of Scientific Computing|2022
118 cit.

Current Senior Leaders

Established & currently active

Current Senior Leaders Researchers

1
George Em Karniadakis
John Brown University
2
Halbert White
University of California San Diego
3
Ruslan Salakhutdinov
Massachusetts Institute of Technology
4
J. Nathan Kutz
University of Washington Applied Physics Laboratory
5
Adam Lerer
OpenAI (United States)
6
Karen Willcox
The University of Texas at Austin
7
Gianluigi Rozza
Scuola Internazionale Superiore di Studi Avanzati
8
Raia Hadsell
Google DeepMind (United Kingdom)
9
Steven L. Brunton
University of Washington
10
Federico Girosi
Intel (United States)

Current Senior Leaders Papers

1
Dropout: a simple way to prevent neural networks from overfitting
Unknown Journal|2014
34.3k cit.
2
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
Econometrica|1980
26.1k cit.
3
Multilayer feedforward networks are universal approximators
Neural Networks|1989
21.2k cit.
4
Reducing the Dimensionality of Data with Neural Networks
Science|2006
21k cit.
5
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Journal of Computational Physics|2018
16.9k cit.
6
Automatic differentiation in PyTorch
Unknown Journal|2017
11.1k cit.
7
Overcoming catastrophic forgetting in neural networks
Proceedings of the National Academy of Sciences|2017
7.2k cit.
8
Dimensionality Reduction by Learning an Invariant Mapping
Unknown Journal|2006
5.2k cit.
9
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
SIAM Journal on Scientific Computing|2002
4.7k cit.
10
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Proceedings of the National Academy of Sciences|2016
4.5k cit.

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