3 citations · 4 across the 8 of their papers we have counts for
9 papers
Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system
Luning Sun, Ben Zhu, Xin-Yang Liu +2
Control of plasma turbulence remains a long-standing challenge in magnetically confined fusion research. Here, we explore a reinforcement-learning (RL) approach for bidirectional c…
Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction
Deepak Akhare, Luning Sun, Xin-Yang Liu +4
Active flow control is a fundamental application in engineering. Recent advances in deep reinforcement learning have made progress in this field. However, the classical online RL a…
High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention
Deepak Akhare, Mohammad Amin Nabian, Corey Adams +2
Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations and energy dissipation throug…
Automotive Crash Dynamics Modeling Accelerated with Machine Learning
Mohammad Amin Nabian, Sudeep Chavare, Deepak Akhare +3
Crashworthiness assessment is a critical aspect of automotive design, traditionally relying on high-fidelity finite element (FE) simulations that are computationally expensive and…
Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
Sanket Jantre, Deepak Akhare, Zhiyuan Wang +2
Partial differential equations (PDEs) underpin the modeling of many natural and engineered systems. It can be convenient to express such models as neural PDEs rather than using tra…
Implicit Neural Differential Model for Spatiotemporal Dynamics
Deepak Akhare, Pan Du, Tengfei Luo +1
Hybrid neural-physics modeling frameworks through differentiable programming have emerged as powerful tools in scientific machine learning, enabling the integration of known physic…
Neural Differentiable Modeling with Diffusion-Based Super-resolution for Two-Dimensional Spatiotemporal Turbulence
Xiantao Fan, Deepak Akhare, Jian-Xun Wang
Simulating spatiotemporal turbulence with high fidelity remains a cornerstone challenge in computational fluid dynamics (CFD) due to its intricate multiscale nature and prohibitive…
DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling
Deepak Akhare, Tengfei Luo, Jian-Xun Wang
The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning. These models, integrating numerical representations of known phy…
Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process
Deepak Akhare, Zeping Chen, Richard Gulotty +2
Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites. These materials are especially…