3 citations · 4 across the 5 of their papers we have counts for
6 papers
Retrainable physics-integrated neural differentiable modeling of sintering across material systems
Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan +1
Sintering is widely used to manufacture ceramics, but coupled densification and grain growth, material-dependent kinetics, and sparse measurements complicate predictive modeling an…
Predicting Stress and Damage in Carbon Fiber-Reinforced Composites Deformation Process using Composite U-Net Surrogate Model
Zeping Chen, Marwa Yacouti, Maryam Shakiba +3
Carbon fiber-reinforced composites (CFRC) are pivotal in advanced engineering applications due to their exceptional mechanical properties. A deep understanding of CFRC behavior und…
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…
JAX-BTE: A GPU-Accelerated Differentiable Solver for Phonon Boltzmann Transport Equations
Wenjie Shang, Jiahang Zhou, J. P. Panda +5
This paper introduces JAX-BTE, a GPU-accelerated, differentiable solver for the phonon Boltzmann Transport Equation (BTE) based on differentiable programming. JAX-BTE enables accur…
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…