Key papers

• Next Generation Equation-Free Multiscale Modelling of Crowd Dynamics via Machine Learning (2025)
  Hector Vargas Alvarez et al.
  3.92
• Dynamic Online Processor-Native Inference for State Estimation (2026)
  Orestis Kaparounakis
  3.51
• LatentFlow: A General Framework for Conditioning Stochastic Processes (2026)
  Louis Sharrock et al.
  3.51
• Ensemble Controlled-Flow Filtering for Implicit Data Assimilation (2026)
  Zhuoyuan Li et al.
  3.51
• DUE: A Deep Learning Framework and Library for Modeling Unknown Equations (2025)
  Junfeng Chen et al.
  2.76
• MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data (2026)
  Liyao Lyu and Xinyue Yu and Hayden Schaeffer
  1.83
• Neural Operators Can Discover Functional Clusters (2026)
  Yicen Li et al.
  1.72
• The Universe Learning Itself: On the Evolution of Dynamics from the Big Bang to Machine Intelligence (2025)
  Pradeep Singh et al.
  1.61
• Stabilization of nonlinear systems with unknown delays via delay-adaptive neural operator approximate predictors (2025)
  Luke Bhan et al.
  1.44
• Fast and Flexible Probabilistic Forecasting of Dynamical Systems using Flow Matching and Physical Perturbation (2025)
  Siddharth Rout et al.
  1.39
• A Score-based Diffusion Model Approach for Adaptive Learning of Stochastic Partial Differential Equation Solutions (2025)
  Toan Huynh et al.
  1.39
• Cost-effective Reduced-Order Modeling via Bayesian Active Learning (2025)
  Amir Hossein Rahmati et al.
  1.28
• Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics (2025)
  Alexander Scheinker
  1.00
• Kinetically Consistent Coarse Graining using Kernel-based Extended Dynamic Mode Decomposition (2024)
  Vahid Nateghi and Feliks N\"uske
  0.83
• Data-driven Closures & Assimilation for Stiff Multiscale Random Dynamics (2023)
  Tyler E. Maltba et al.
  0.33
• Multifidelity Computing for Coupling Full and Reduced Order Models (2020)
  Shady E. Ahmed et al.
  —
• Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach (2022)
  Evangelos Galaris et al.
  —
• Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems (2022)
  Muhammad Umar B. Niazi et al.
  —
• Estimating Koopman operators with sketching to provably learn large scale dynamical systems (2023)
  Giacomo Meanti et al.
  —
• Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems (2023)
  Marco Lepri et al.
  —
• Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data (2024)
  Meghana Velegar et al.
  —
• Automating the Discovery of Partial Differential Equations in Dynamical Systems (2024)
  Weizhen Li and Rui Carvalho
  —
• RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators (2024)
  Gianluca Fabiani et al.
  —
• A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics (2024)
  Junqi Yin et al.
  —
• Extracting Signal out of Chaos: Advancements on MAGI for Bayesian Analysis of Dynamical Systems (2024)
  Skyler Wu
  —
• S7: Selective and Simplified State Space Layers for Sequence Modeling (2024)
  Taylan Soydan et al.
  —
• On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks (2024)
  Yihang Gao et al.
  —