5 papers
MonoPhysics: Estimating Geometry, Appearance, and Physical Parameters from Monocular Videos
Daniel Rho, Jun Myeong Choi, Matthew Thornton +2
Existing inverse physics methods recover physical parameters from multi-view videos, where geometric constraints across views resolve scale and 3D structure. In monocular settings,…
ProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation
Daniel Rho, Jun Myeong Choi, Biswadip Dey +1
Neural rendering has advanced significantly in 3D reconstruction and novel view synthesis, and integrating physics into these frameworks opens new applications such as physically a…
Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
Ankur Kamboj, Biswadip Dey, Vaibhav Srivastava
We develop a physics-informed learning framework for energy-shaping control of port-Hamiltonian (pH) systems from trajectory data. The proposed approach co-learns a pH system model…
Geometry-aware PINNs for Turbulent Flow Prediction
Shinjan Ghosh, Julian Busch, Georgia Olympia Brikis +1
Design exploration or optimization using computational fluid dynamics (CFD) is commonly used in the industry. Geometric variation is a key component of such design problems, especi…
Using Parametric PINNs for Predicting Internal and External Turbulent Flows
Shinjan Ghosh, Amit Chakraborty, Georgia Olympia Brikis +1
Computational fluid dynamics (CFD) solvers employing two-equation eddy viscosity models are the industry standard for simulating turbulent flows using the Reynolds-averaged Navier-…