Key papers

• Towards Generalization of Graph Neural Networks for AC Optimal Power Flow (2025)
  Olayiwola Arowolo et al.
  5.21
• Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence (2026)
  Abdullah Al Tasim et al.
  4.39
• Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch (2026)
  Yangze Zhou et al.
  4.39
• Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms (2026)
  Marcos Eduardo Cruz Victorio et al.
  4.39
• Gradient-free learning of a closed-loop wall controller for turbulent drag reduction (2026)
  Giorgio Maria Cavallazzi et al.
  4.39
• Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority (2026)
  Philip-Roman Adam et al.
  4.39
• Variational meta-learning inference for low dimensional neural system identification (2026)
  Matteo Rufolo et al.
  4.39
• Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability (2026)
  Zhong Zhang et al.
  4.27
• Stable Port-Hamiltonian Neural Networks (2025)
  Fabian J. Roth et al.
  3.59
• Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies (2026)
  Ziheng Cheng et al.
  3.51
• Market Strategy Evaluation for Prosumers in Local Electricity Markets (2026)
  Lukas Peter Wagner et al.
  3.51
• The Open Ant: A Robot Platform for Reinforcement Learning Research (2026)
  Elena Sorina Lupu et al.
  3.51
• From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs (2026)
  Jason Stanley (UC San Diego et al.
  3.51
• Neural Spline Operators for Risk Quantification in Stochastic Systems (2025)
  Zhuoyuan Wang et al.
  2.99
• Scalable Perturbation Learning for Online Self-Supervised Learning in Echo State Networks (2026)
  Taiki Yamada et al.
  2.00
• A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields (2026)
  Sha (Sasha) et al.
  2.00
• Neural Operators for Design-Space Surrogate Modeling of Tendon-Actuated Continuum Robots (2026)
  Branden Frieden et al.
  1.89
• Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems (2026)
  Mohammed Ezzaldin Babiker Abdullah
  1.83
• Joint Surrogate Learning of Objectives, Constraints, and Sensitivities for Efficient Multi-objective Optimization of Neural Dynamical Systems (2026)
  Frithjof Gressmann et al.
  1.78
• BOOST-RPF: Boosted Sequential Trees for Radial Power Flow (2026)
  Ehimare Okoyomon et al.
  1.78
• Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study (2026)
  Orfeas Bourchas et al.
  1.72
• Physics Informed Reconstruction of Four-Dimensional Atmospheric Wind Fields Using Multi-UAS Swarm Observations in a Synthetic Turbulent Environment (2026)
  Abdullah Tasim and Wei Sun
  1.67
• Restless Multi-Process Multi-Armed Bandits with Applications to Self-Driving Microscopies (2025)
  Jaume Anguera Peris et al.
  1.61
• Sparse-to-Field Reconstruction via Stochastic Neural Dynamic Mode Decomposition (2025)
  Yujin Kim and Sarah Dean
  1.56
• Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions (2025)
  Muhy Eddin Za'ter et al.
  1.50
• Stabilization of nonlinear systems with unknown delays via delay-adaptive neural operator approximate predictors (2025)
  Luke Bhan et al.
  1.44
• Toward accurate RUL and SOH estimation using reinforced graph-based PINNs enhanced with dynamic weights (2025)
  Mohamadreza Akbari Pour et al.
  1.33
• Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles (2025)
  Abdullah Burkan Bereketoglu
  1.28
• Cost-effective Reduced-Order Modeling via Bayesian Active Learning (2025)
  Amir Hossein Rahmati et al.
  1.28
• Learning-Augmented Power System Operations: A Unified Optimization View (2025)
  Wangkun Xu et al.
  1.22
• Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks (2025)
  Satoshi Sunada and Tomoaki Niiyama and Kazutaka Kanno and Rin Nogami and Andr\'e R\"ohm and Takato Awano and Atsushi Uchida
  1.06
• (Un)supervised Learning of Maximal Lyapunov Functions (2024)
  Matthieu Barreau et al.
  0.78
• Generalizable Physics-Informed Learning for Stochastic Safety-Critical Systems (2024)
  Zhuoyuan Wang et al.
  0.72
• One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION (2023)
  Cesare Donati et al.
  0.22
• Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method (2017)
  Hao Li and Zhijian Liu
  —
• Evaluation of electrical efficiency of photovoltaic thermal solar collector (2020)
  Mohammad Hossein Ahmadi et al.
  —
• Secondary control activation analysed and predicted with explainable AI (2021)
  Johannes Kruse et al.
  —
• gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification (2022)
  Xiaolong He et al.
  —
• Machine Learning for Improved Gas Network Models in Coordinated Energy Systems (2022)
  Adriano Arrigo et al.
  —
• Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems (2022)
  Muhammad Umar B. Niazi et al.
  —
• Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks (2022)
  Cyrus Neary and Ufuk Topcu
  —
• Comparative Study of MPPT and Parameter Estimation of PV cells (2023)
  Sahil Kumar et al.
  —
• Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems (2023)
  Marco Lepri et al.
  —
• LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction (2024)
  Jun Liu et al.
  —
• Unsupervised machine learning for data-driven rock mass classification: addressing limitations in existing systems using drilling data (2024)
  T. F. Hansen et al.
  —
• Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis (2024)
  Geremy Loacham\'in Suntaxi et al.
  —
• Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach (2024)
  Giacomo Acciarini and Laurent Beauregard and Dario Izzo
  —
• Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models (2024)
  Kaiyuan Tan et al.
  —
• AI Enabled Neutron Flux Measurement and Virtual Calibration in Boiling Water Reactors (2024)
  Anirudh Tunga et al.
  —
• Hessian-Informed Flow Matching (2024)
  Christopher Iliffe Sprague et al.
  —
• AI-Driven Autonomous Control of Proton-Boron Fusion Reactors Using Backpropagation Neural Networks (2024)
  Michele Laurelli
  —
• Diffusion Model-based Parameter Estimation in Dynamic Power Systems (2024)
  Feiqin Zhu et al.
  —