3 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction
Zeping Chen, Ruda Jian, Sachin Sigdel +3
Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation. However, purely data-driven models may generalize poorly across…
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…
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…