Cover Image

Physics-informed State-space Neural Networks for Transport Phenomena

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dave, Akshay J., Vilim, Richard B.
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Systems and Control Computational Physics Fluid Dynamics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence
by: Koh, Christopher, et al.
Published: (2024)

Physics-enhanced Neural Operator for Simulating Turbulent Transport
by: Chen, Shengyu, et al.
Published: (2024)

Target Parameterization in Diffusion Models for Nonlinear Spatiotemporal System Identification
by: Messaoudi, Achraf El, et al.
Published: (2026)

Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
by: Mole, Andrew, et al.
Published: (2025)

Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows
by: Ozan, Defne E., et al.
Published: (2025)

Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control
by: Liu, Xin-Yang, et al.
Published: (2024)

Harvesting energy from turbulent winds with Reinforcement Learning
by: Basile, Lorenzo, et al.
Published: (2024)

Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
by: Wang, Sifan, et al.
Published: (2025)

Bayesian Reasoning for Physics Informed Neural Networks
by: Graczyk, Krzysztof M., et al.
Published: (2023)

Higher order quantum reservoir computing for non-intrusive reduced-order models
by: Jain, Vinamr, et al.
Published: (2024)

Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows
by: Cheng, Xinlun, et al.
Published: (2025)

Integrating LLMs for Explainable Fault Diagnosis in Complex Systems
by: Dave, Akshay J., et al.
Published: (2024)

Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy
by: Li, Zhihui, et al.
Published: (2023)

Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder
by: Gupta, Priyam, et al.
Published: (2023)

Hierarchy of extreme-event predictability in turbulence revealed by machine learning
by: Yang, Yuxuan, et al.
Published: (2026)

Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics
by: Suriyanarayanan, Ashwin, et al.
Published: (2026)

Applied Neural Network-Based Active Control for Vortex-Induced Vibrations Suppression in a Two-Degree-of-Freedom Cylinder
by: Ilbeigi, Soha, et al.
Published: (2025)

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs with Physics-Informed Neural Networks
by: Walter, Linus, et al.
Published: (2025)

Physics-informed neural networks for hidden boundary detection and flow field reconstruction
by: Zhu, Yongzheng, et al.
Published: (2025)

Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows
by: Zhao, Zelin, et al.
Published: (2025)

A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants
by: Sun, Yixuan, et al.
Published: (2024)

Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations
by: Xue, Tingkai, et al.
Published: (2025)

Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
by: Barwey, Shivam, et al.
Published: (2023)

Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks
by: Ortali, Giulio, et al.
Published: (2024)

Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks
by: Toscano, Juan Diego, et al.
Published: (2024)

PINNfluence: Influence Functions for Physics-Informed Neural Networks
by: Naujoks, Jonas R., et al.
Published: (2024)

Neural equilibria for long-term prediction of nonlinear conservation laws
by: Benitez, J. Antonio Lara, et al.
Published: (2025)

Extended Physics Informed Neural Network for Hyperbolic Two-Phase Flow in Porous Media
by: Rehman, Saif Ur, et al.
Published: (2025)

Symplectic Neural Flows for Modeling and Discovery
by: Canizares, Priscilla, et al.
Published: (2024)

Predicting The Evolution of Interfaces with Fourier Neural Operators
by: Guida, Paolo, et al.
Published: (2025)

Error estimation for physics-informed neural networks with implicit Runge-Kutta methods
by: Stiasny, Jochen, et al.
Published: (2024)

From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
by: Riva, Stefano, et al.
Published: (2025)

Learning rheological parameters of non-Newtonian fluids from velocimetry data
by: Kontogiannis, Alexandros, et al.
Published: (2024)

Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model
by: Xue, Xiao, et al.
Published: (2025)

Neural Networks for Predicting Permeability Tensors of 2D Porous Media: Comparison of Convolution- and Transformer-based Architectures
by: Vargdal, Sigurd, et al.
Published: (2025)

Short-term wind forecasting via surface pressure measurements: stochastic modeling and sensor placement
by: Abootorabi, Seyedalireza, et al.
Published: (2024)

Physics-Informed Dynamical Modeling of Extrusion-Based 3D Printing Processes
by: Looey, Mandana Mohammadi, et al.
Published: (2025)

Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows
by: Barwey, Shivam, et al.
Published: (2023)

Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?
by: Guglielmo, Gianmarco, et al.
Published: (2024)

A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data
by: Sorensen, Benedikt Barthel, et al.
Published: (2024)