Key papers

• Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces (2026)
  Domiziano Doria et al.
  4.39
• A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems (2026)
  Sonal Ankush Chibire et al.
  4.39
• Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion (2026)
  Jiatong Zhao et al.
  4.39
• Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards (2026)
  Pengfei Cai et al.
  4.39
• Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion (2026)
  Haoze Zhang et al.
  4.39
• Online model learning with data-assimilated reservoir computers (2025)
  Andrea N\'ovoa et al.
  3.70
• Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension (2026)
  R\"udiger Kempf
  3.51
• Physics-Informed Recurrent Architecture with Embedded Thermodynamic Dynamics for Robust Sequence Modeling (2026)
  Zafer Yiğit et al.
  1.94
• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting (2026)
  Hao Wu et al.
  1.89
• DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting (2026)
  Yeran Zhang et al.
  1.83
• Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computing (2026)
  Xin Li et al.
  1.78
• Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder (2026)
  Kewei Zhu et al.
  1.78
• Continuous-Time Piecewise-Linear Recurrent Neural Networks (2026)
  Alena Br\"andle et al.
  1.72
• Neural CDEs as Correctors for Learned Time Series Models (2025)
  Muhammad Bilal Shahid et al.
  1.61
• Random Controlled Differential Equations (2025)
  Francesco Piatti et al.
  1.61
• Reservoir Computing via Multi-Scale Random Fourier Features for Forecasting Fast-Slow Dynamical Systems (2025)
  S. K. Laha
  1.56
• Accelerating Long-Term Molecular Dynamics with Physics-Informed Time-Series Forecasting (2025)
  Hung Le et al.
  1.50
• Modeling Time Series Dynamics with Fourier Ordinary Differential Equations (2025)
  Muhao Guo et al.
  1.50
• Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution (2025)
  Hamidreza Razavi et al.
  1.44
• Learning Spatio-Temporal Dynamics via Operator-Valued RKHS and Kernel Koopman Methods (2025)
  Mahishanka Withanachchi
  1.39
• Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics (2025)
  Alex Guo et al.
  1.33
• Continuous-Time SO(3) Forecasting with Savitzky--Golay Neural Controlled Differential Equations (2025)
  Lennart Bastian et al.
  1.28
• Infinite-dimensional next-generation reservoir computing (2024)
  Lyudmila Grigoryeva et al.
  1.00
• Recursive Gaussian Process State Space Model (2024)
  Tengjie Zheng et al.
  0.94
• Latent Space Energy-based Neural ODEs (2024)
  Sheng Cheng et al.
  0.83
• NOMADS: Non-Markovian Optimization-based Modeling for Approximate Dynamics with Spatially-homogeneous Memory (2024)
  Ryoji Anzaki and Kazuhiro Sato
  0.83
• DeNOTS: Stable Deep Neural ODEs for Time Series (2024)
  Ilya Kuleshov et al.
  0.78
• Oscillations enhance time-series prediction in reservoir computing with feedback (2024)
  Yuji Kawai and Takashi Morita and Jihoon Park and Minoru Asada
  0.67
• Reservoir Computing on the Hypersphere (2017)
  M. Andrecut
  —
• Emulating dynamic non-linear simulators using Gaussian processes (2018)
  Hossein Mohammadi et al.
  —
• Learning Causally-Generated Stationary Time Series (2018)
  Wessel Bruinsma and Richard E. Turner
  —
• Rapid Time Series Prediction with a Hardware-Based Reservoir Computer (2018)
  Daniel Canaday et al.
  —
• Infinite-Horizon Gaussian Processes (2018)
  Arno Solin et al.
  —
• Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations (2019)
  Mohammad Javad Eslamibidgoli et al.
  —
• Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics (2019)
  Pantelis R. Vlachas et al.
  —
• Interpretable Super-Resolution via a Learned Time-Series Representation (2020)
  Randall Balestriero et al.
  —
• Forecasting Hamiltonian dynamics without canonical coordinates (2020)
  Anshul Choudhary et al.
  —
• Weak Form Generalized Hamiltonian Learning (2021)
  Kevin L. Course et al.
  —
• Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics (2021)
  Gouhei Tanaka et al.
  —
• Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning (2022)
  James Andrews et al.
  —
• Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing (2022)
  Wendson A. S. Barbosa and Daniel J. Gauthier
  —
• Constraints on parameter choices for successful reservoir computing (2022)
  L. Storm et al.
  —
• Prediction of good reaction coordinates and future evolution of MD trajectories using Regularized Sparse Autoencoders: A novel deep learning approach (2022)
  Abhijit Gupta
  —
• Learning PDE Solution Operator for Continuous Modeling of Time-Series (2023)
  Yesom Park et al.
  —
• Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections (2023)
  Edward De Brouwer and Rahul G. Krishnan
  —
• Faster Training of Neural ODEs Using Gau{\ss}-Legendre Quadrature (2023)
  Alexander Norcliffe et al.
  —
• ODE-based Recurrent Model-free Reinforcement Learning for POMDPs (2023)
  Xuanle Zhao et al.
  —
• Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods (2023)
  Chuanqi Chen et al.
  —
• Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems (2024)
  Ravi Chepuri et al.
  —
• Spacecraft inertial parameters estimation using time series clustering and reinforcement learning (2024)
  Konstantinos Platanitis et al.
  —
• Universality of Real Minimal Complexity Reservoir (2024)
  Robert Simon Fong et al.
  —
• Toward Physics-guided Time Series Embedding (2024)
  Jiaxi Hu et al.
  —