Copertina

Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Becktepe, Jannis, Franz, Aleksandra, Thuerey, Nils, Peitz, Sebastian
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision
di: Franz, Aleksandra, et al.
Pubblicazione: (2023)

tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
di: Xie, You, et al.
Pubblicazione: (2018)

PICT -- A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics
di: Franz, Aleksandra, et al.
Pubblicazione: (2025)

INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers
di: Wei, Hao, et al.
Pubblicazione: (2025)

Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning
di: Straat, Michiel, et al.
Pubblicazione: (2025)

Flow Matching for Posterior Inference with Simulator Feedback
di: Holzschuh, Benjamin, et al.
Pubblicazione: (2024)

Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
di: Kohl, Georg, et al.
Pubblicazione: (2023)

ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
di: Becktepe, Jannis, et al.
Pubblicazione: (2024)

Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction
di: Wang, Shaofan, et al.
Pubblicazione: (2025)

Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data
di: Koehler, Felix, et al.
Pubblicazione: (2025)

Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime
di: Markmann, Thorben, et al.
Pubblicazione: (2025)

Stabilizing Backpropagation Through Time to Learn Complex Physics
di: Schnell, Patrick, et al.
Pubblicazione: (2024)

Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics
di: Winchenbach, Rene, et al.
Pubblicazione: (2024)

Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
di: Hertrich, Johannes, et al.
Pubblicazione: (2020)

The Unreasonable Effectiveness of Solving Inverse Problems with Neural Networks
di: Holl, Philipp, et al.
Pubblicazione: (2024)

APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs
di: Koehler, Felix, et al.
Pubblicazione: (2024)

Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models
di: Liu, Qiang, et al.
Pubblicazione: (2023)

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
di: Peitz, Sebastian, et al.
Pubblicazione: (2024)

Plug-and-Play Controllable Generation for Discrete Masked Models
di: Guo, Wei, et al.
Pubblicazione: (2024)

PRDP: Progressively Refined Differentiable Physics
di: Bhatia, Kanishk, et al.
Pubblicazione: (2025)

PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator
di: Yan, Hanshu, et al.
Pubblicazione: (2024)

Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection
di: Plotzki, Tim, et al.
Pubblicazione: (2026)

SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion
di: Hotegni, Sedjro Salomon, et al.
Pubblicazione: (2025)

Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
di: Zhang, Xiangyuan, et al.
Pubblicazione: (2023)

Global Transport for Fluid Reconstruction with Learned Self-Supervision
di: Franz, Aleksandra, et al.
Pubblicazione: (2021)

Enhancing Adversarial Robustness through Multi-Objective Representation Learning
di: Hotegni, Sedjro Salomon, et al.
Pubblicazione: (2024)

Momentum Guidance: Plug-and-Play Guidance for Flow Models
di: Liao, Runlong, et al.
Pubblicazione: (2026)

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning
di: Winchenbach, Rene, et al.
Pubblicazione: (2025)

PnP-Flow: Plug-and-Play Image Restoration with Flow Matching
di: Martin, Ségolène, et al.
Pubblicazione: (2024)

Bias-Constrained Diffusion Schedules for PDE Emulations: Reconstruction Error Minimization and Efficient Unrolled Training
di: Cleï, Constantin Le, et al.
Pubblicazione: (2026)

ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks
di: Liu, Qiang, et al.
Pubblicazione: (2024)

Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning
di: Liu, Qiang, et al.
Pubblicazione: (2026)

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems
di: Goyal, Girnar, et al.
Pubblicazione: (2025)

InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences
di: Zheng, Hongkai, et al.
Pubblicazione: (2025)

Automatic feature identification in least-squares policy iteration using the Koopman operator framework
di: Zagabe, Christian Mugisho, et al.
Pubblicazione: (2026)

PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolation
di: Wu, Xuhang, et al.
Pubblicazione: (2026)

Physics vs Distributions: Pareto Optimal Flow Matching with Physics Constraints
di: Baldan, Giacomo, et al.
Pubblicazione: (2025)

FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control
di: Ye, Yutong, et al.
Pubblicazione: (2025)

PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
di: Holzschuh, Benjamin, et al.
Pubblicazione: (2025)

P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context
di: Holzschuh, Benjamin, et al.
Pubblicazione: (2025)