Search Results for author:
Found 16 papers, 6 papers with code
Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control
Despite extensive research on the kinematics and hydrodynamics of fish locomotion, the intricate control strategies in fin-ray actuation remain largely unexplored.
DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling
Addressing this gap, we introduce a novel method, DiffHybrid-UQ, for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models, leveraging the strengths of deep ensemble Bayesian learning and nonlinear transformations.
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
A notable feature of our approach is the method proposed for long-span flow sequence generation, which is based on autoregressive gradient-based conditional sampling, eliminating the need for cumbersome retraining processes.
Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process
Due to the complexities and limited experimental data of the isothermal CVI densification process, we have developed a data-driven predictive model using the physics-integrated neural differentiable (PiNDiff) modeling framework.
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations
Recently the Transformer structure has shown good performances in graph learning tasks.
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
In this paper, we present a novel physics-informed neural network (PINN) model for seismic wave modeling in semi-infinite domain without the nedd of labeled data.
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
The equation residuals are used to inform the spline learning in a Bayesian manner, where approximate Bayesian uncertainty calibration techniques are employed to approximate posterior distributions of the trainable parameters.
Physics-informed Deep Super-resolution for Spatiotemporal Data
High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales.
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search
The key concept is to interpret mathematical operations and system state variables by computational rules and symbols, establish symbolic reasoning of mathematical formulas via expression trees, and employ a Monte Carlo tree search (MCTS) agent to explore optimal expression trees based on measurement data.
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes.
Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics
An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces.
Predicting Physics in Mesh-reduced Space with Temporal Attention
Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes.
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control
Model-based reinforcement learning (MBRL) is believed to have much higher sample efficiency compared to model-free algorithms by learning a predictive model of the environment.
Model-based Reinforcement Learning
reinforcement-learning
+1
Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data
In many applications, flow measurements are usually sparse and possibly noisy.
Computational Physics Data Analysis, Statistics and Probability Fluid Dynamics
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning
This study demonstrates that the PIML approach is a computationally affordable technique for improving the accuracy of RANS-modeled Reynolds stresses for high-Mach-number turbulent flows when there is a lack of experiments and high-fidelity simulations.
BIG-bench Machine Learning
Physics-informed machine learning
A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
In this work, we introduce the procedures toward a complete PIML framework for predictive turbulence modeling, including learning Reynolds stress discrepancy function, predicting Reynolds stresses in different flows, and propagating to mean flow fields.
Fluid Dynamics
