Cover Image

Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Okoyomon, Ehimare, Yaniv, Arbel, Goebel, Christoph
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
by: Okoyomon, Ehimare, et al.
Published: (2026)

Exploring Variational Graph Autoencoders for Distribution Grid Data Generation
by: Abbas, Syed Zain, et al.
Published: (2025)

Robust and Interpretable Graph Neural Networks for Power Systems State Estimation
by: Yaniv, Arbel, et al.
Published: (2026)

GenTL: A General Transfer Learning Model for Building Thermal Dynamics
by: Raisch, Fabian, et al.
Published: (2025)

Physics-Informed Learning of Proprietary Inverter Models for Grid Dynamic Studies
by: Kwon, Kyung-Bin, et al.
Published: (2025)

Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
by: Dogoulis, Pantelis, et al.
Published: (2026)

Transfer Learning for Neural Parameter Estimation applied to Building RC Models
by: Raisch, Fabian, et al.
Published: (2026)

Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts
by: Raisch, Fabian, et al.
Published: (2025)

Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models
by: de Vargas, Jan Marco Ruiz, et al.
Published: (2026)

Multilayer GNN for Predictive Maintenance and Clustering in Power Grids
by: Kazim, Muhammad, et al.
Published: (2025)

Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders
by: Sundsgaard, Konrad, et al.
Published: (2025)

PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain
by: Cai, Xiaoyi, et al.
Published: (2024)

Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems
by: Abdullah, Mohammed Ezzaldin Babiker
Published: (2026)

Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee
by: Xiao, Chenhan, et al.
Published: (2023)

Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems
by: Ouyang, Zhuo, et al.
Published: (2026)

Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering
by: Feng, Zijian, et al.
Published: (2023)

Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
by: Nauck, Christian, et al.
Published: (2024)

Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network
by: Sun, Peiyuan, et al.
Published: (2022)

Physics-Informed Deep B-Spline Networks
by: Wang, Zhuoyuan, et al.
Published: (2025)

Formally Verified Physics-Informed Neural Control Lyapunov Functions
by: Liu, Jun, et al.
Published: (2024)

Learning a Generalized Model for Substation Level Voltage Estimation in Distribution Networks
by: Za'ter, Muhy Eddin, et al.
Published: (2025)

Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
by: Ramirez, Ibai, et al.
Published: (2025)

Generalizable Physics-Informed Learning for Stochastic Safety-Critical Systems
by: Wang, Zhuoyuan, et al.
Published: (2024)

Verifiable Error Bounds for Physics-Informed Neural KKL Observers
by: Berin-Costain, Hannah, et al.
Published: (2026)

Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
by: Liu, Zheng
Published: (2026)

State and Action Factorization in Power Grids
by: Losapio, Gianvito, et al.
Published: (2024)

Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
by: Authier, Jules, et al.
Published: (2023)

Learning Robust Satellite Attitude Dynamics with Physics-Informed Normalising Flow
by: Cena, Carlo, et al.
Published: (2025)

KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning
by: Niazi, M. Umar B., et al.
Published: (2025)

Physics-Informed Neural Networks for Accelerating Power System State Estimation
by: Falas, Solon, et al.
Published: (2023)

Solving Differential Equations using Physics-Informed Deep Equilibrium Models
by: Pacheco, Bruno Machado, et al.
Published: (2024)

A Survey on Physics Informed Reinforcement Learning: Review and Open Problems
by: Banerjee, Chayan, et al.
Published: (2023)

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
by: Jin, Hongwei, et al.
Published: (2024)

A KAN-based Interpretable Framework for Process-Informed Prediction of Global Warming Potential
by: Lee, Jaewook, et al.
Published: (2024)

Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification
by: Liu, Jun, et al.
Published: (2023)

Online Intention Prediction via Control-Informed Learning
by: Zhou, Tianyu, et al.
Published: (2026)

Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies
by: Pareek, Parikshit, et al.
Published: (2023)

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
by: Ito, Junsei, et al.
Published: (2025)

Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
by: Li, Sirui, et al.
Published: (2025)

Robust Power System State Estimation using Physics-Informed Neural Networks
by: Falas, Solon, et al.
Published: (2025)