Optimal Power Grid Operations with Foundation Models
Fuente:
arXiv
Guardado en:
| Autores principales: | Puech, Alban, Weiss, Jonas, Brunschwiler, Thomas, Hamann, Hendrik F. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
por: Puech, Alban, et al.
Publicado: (2025)
por: Puech, Alban, et al.
Publicado: (2025)
Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and Stochastic Programming
por: Puech, Alban, et al.
Publicado: (2024)
por: Puech, Alban, et al.
Publicado: (2024)
Foundation Models for the Electric Power Grid
por: Hamann, Hendrik F., et al.
Publicado: (2024)
por: Hamann, Hendrik F., et al.
Publicado: (2024)
Optimal Control Operator Perspective and a Neural Adaptive Spectral Method
por: Feng, Mingquan, et al.
Publicado: (2024)
por: Feng, Mingquan, et al.
Publicado: (2024)
PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
por: Klamkin, Michael, et al.
Publicado: (2025)
por: Klamkin, Michael, et al.
Publicado: (2025)
Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
por: Rosemberg, Andrew, et al.
Publicado: (2025)
por: Rosemberg, Andrew, et al.
Publicado: (2025)
Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
por: Skifstad, Julian, et al.
Publicado: (2026)
por: Skifstad, Julian, et al.
Publicado: (2026)
Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
por: Ibrahim, Sinan, et al.
Publicado: (2026)
por: Ibrahim, Sinan, et al.
Publicado: (2026)
Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
por: Buyuktahtakin, I. Esra
Publicado: (2026)
por: Buyuktahtakin, I. Esra
Publicado: (2026)
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
por: Ding, Jianglin, et al.
Publicado: (2025)
por: Ding, Jianglin, et al.
Publicado: (2025)
Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
por: Kamboj, Ankur, et al.
Publicado: (2026)
por: Kamboj, Ankur, et al.
Publicado: (2026)
Adaptive Neural-Operator Backstepping Control of a Benchmark Hyperbolic PDE
por: Lamarque, Maxence, et al.
Publicado: (2024)
por: Lamarque, Maxence, et al.
Publicado: (2024)
Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation
por: Lamarque, Maxence, et al.
Publicado: (2024)
por: Lamarque, Maxence, et al.
Publicado: (2024)
Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees
por: Nath, Devesh, et al.
Publicado: (2026)
por: Nath, Devesh, et al.
Publicado: (2026)
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making
por: Asru, Avijit Saha, et al.
Publicado: (2025)
por: Asru, Avijit Saha, et al.
Publicado: (2025)
Synergies between Federated Foundation Models and Smart Power Grids
por: Hosseinalipour, Seyyedali, et al.
Publicado: (2025)
por: Hosseinalipour, Seyyedali, et al.
Publicado: (2025)
Neural Hamiltonian Operator
por: Qi, Qian
Publicado: (2025)
por: Qi, Qian
Publicado: (2025)
A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks
por: Cayci, Semih
Publicado: (2024)
por: Cayci, Semih
Publicado: (2024)
Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
por: Ozaslan, Ibrahim K., et al.
Publicado: (2024)
por: Ozaslan, Ibrahim K., et al.
Publicado: (2024)
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems
por: Huang, Yilie, et al.
Publicado: (2024)
por: Huang, Yilie, et al.
Publicado: (2024)
Primitive Agentic First-Order Optimization
por: Sala, R.
Publicado: (2024)
por: Sala, R.
Publicado: (2024)
Differentiable Distributionally Robust Optimization Layers
por: Ma, Xutao, et al.
Publicado: (2024)
por: Ma, Xutao, et al.
Publicado: (2024)
Generative AI and Process Systems Engineering: The Next Frontier
por: Decardi-Nelson, Benjamin, et al.
Publicado: (2024)
por: Decardi-Nelson, Benjamin, et al.
Publicado: (2024)
Fitted Q-Iteration via Max-Plus-Linear Approximation
por: Liu, Y., et al.
Publicado: (2024)
por: Liu, Y., et al.
Publicado: (2024)
Stochastic Learning of Computational Resource Usage as Graph Structured Multimarginal Schrödinger Bridge
por: Bondar, Georgiy A., et al.
Publicado: (2024)
por: Bondar, Georgiy A., et al.
Publicado: (2024)
PID Accelerated Temporal Difference Algorithms
por: Bedaywi, Mark, et al.
Publicado: (2024)
por: Bedaywi, Mark, et al.
Publicado: (2024)
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
por: Ju, Caleb, et al.
Publicado: (2024)
por: Ju, Caleb, et al.
Publicado: (2024)
On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks
por: de Oliveira, Arthur Castello Branco, et al.
Publicado: (2025)
por: de Oliveira, Arthur Castello Branco, et al.
Publicado: (2025)
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
por: Zhu, Feng, et al.
Publicado: (2025)
por: Zhu, Feng, et al.
Publicado: (2025)
Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO
por: Esfahani, Hossein Nejatbakhsh, et al.
Publicado: (2025)
por: Esfahani, Hossein Nejatbakhsh, et al.
Publicado: (2025)
WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
por: Suri, Dhruv, et al.
Publicado: (2026)
por: Suri, Dhruv, et al.
Publicado: (2026)
Nonlinear Non-Gaussian Density Steering with Input and Noise Channel Mismatch: Sinkhorn with Memory for Solving the Control-affine Schrödinger Bridge Problem
por: Bondar, Georgiy A., et al.
Publicado: (2026)
por: Bondar, Georgiy A., et al.
Publicado: (2026)
Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks
por: Islam, MD Shafikul, et al.
Publicado: (2023)
por: Islam, MD Shafikul, et al.
Publicado: (2023)
Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning
por: Ma, Chaolun, et al.
Publicado: (2022)
por: Ma, Chaolun, et al.
Publicado: (2022)
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
por: Jing, Gangshan, et al.
Publicado: (2021)
por: Jing, Gangshan, et al.
Publicado: (2021)
Faster Reinforcement Learning by Freezing Slow States
por: Wang, Yijia, et al.
Publicado: (2023)
por: Wang, Yijia, et al.
Publicado: (2023)
ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
por: Huang, Yilie, et al.
Publicado: (2026)
por: Huang, Yilie, et al.
Publicado: (2026)
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
por: Li, Jingqi, et al.
Publicado: (2022)
por: Li, Jingqi, et al.
Publicado: (2022)
Bucketized Active Sampling for Learning ACOPF
por: Klamkin, Michael, et al.
Publicado: (2022)
por: Klamkin, Michael, et al.
Publicado: (2022)
Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs
por: Ding, Dongsheng, et al.
Publicado: (2022)
por: Ding, Dongsheng, et al.
Publicado: (2022)
Ejemplares similares
-
gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
por: Puech, Alban, et al.
Publicado: (2025) -
Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and Stochastic Programming
por: Puech, Alban, et al.
Publicado: (2024) -
Foundation Models for the Electric Power Grid
por: Hamann, Hendrik F., et al.
Publicado: (2024) -
Optimal Control Operator Perspective and a Neural Adaptive Spectral Method
por: Feng, Mingquan, et al.
Publicado: (2024) -
PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
por: Klamkin, Michael, et al.
Publicado: (2025)