19 papers
Bayesian Posterior Sampling for Synthetic Shape Generation of Heart Valves
Vijay Dubey, Sumedh Seetharam, Nikos Manthatis +9
Statistical shape models (SSMs) for heart valves commonly rely on principal component analysis (PCA). They are used to support downstream tasks, including \textit{in silico} modeli…
Transformer-Based Inverse Microrheology for Experimental Mechanics at Ultra-High Strain Rates
Lehu Bu, Zhaohan Yu, Danila Frolkin +5
Traditional rheological tools are often limited in characterizing soft materials under ultra-high strain-rate loading conditions (> 1000 s^-1) due to constraints in spatiotemporal…
Full-Field Calibration of Coupled Thermomechanical Material Models at Finite Strain
L. River Spencer, William D. Meador, Adrian Buganza Tepole +5
Calibrating thermomechanical material models from experiments is challenging because deformation, temperature, and force responses are strongly coupled, while measurements are usua…
A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling
Reese E. Jones, Jan N. Fuhg
Recent advances in physics-augmented neural networks have enabled thermodynamically consistent data-driven constitutive modeling of complex inelastic materials. Most existing appro…
Multiscale Structural Reliability Analysis in high dimensions with Tensor Trains and Physics-Augmented Neural Networks
Aryan Tyagi, Alex de Beer, Tiangang Cui +1
Structural reliability evaluation for composites constitutes a fundamentally high-dimensional multiscale problem, as microscale material uncertainties must propagate to the macrosc…
Fully Differentiable Ultrasound Simulation Utilizing Ray-Tracing
L. River Spencer, Reagan A. Cardoza, Vijay K. Dubey +5
Ultrasound imaging tasks such as calibration, inverse parameter estimation, and acquisition design require models that are physically grounded, efficient, and differentiable with r…