DiffPhD delivers a unified differentiable projective dynamics solver for heterogeneous hyperelastic elastodynamics with contact that achieves up to 10x speedup and stable convergence on 100x stiffness contrasts while preserving strict gradient accuracy.
Brown, Jie Li, and Rahul Narain
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A new GPU-accelerated deformable simulation framework trains manipulation policies in minutes using only synthetic data, achieving robust zero-shot transfer to physical robots.
The authors develop a differentiable simulator enforcing Markovian dynamics on a position-velocity manifold and using a mass-aligned preconditioner with a soft Fischer-Burmeister operator to produce stable gradients for frictional contact in large-deformation scenarios.
citing papers explorer
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DiffPhD: A Unified Differentiable Solver for Projective Heterogeneous Materials in Elastodynamics with Contact-Rich GPU-Acceleration
DiffPhD delivers a unified differentiable projective dynamics solver for heterogeneous hyperelastic elastodynamics with contact that achieves up to 10x speedup and stable convergence on 100x stiffness contrasts while preserving strict gradient accuracy.
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FLASH: Fast Learning via GPU-Accelerated Simulation for High-Fidelity Deformable Manipulation in Minutes
A new GPU-accelerated deformable simulation framework trains manipulation policies in minutes using only synthetic data, achieving robust zero-shot transfer to physical robots.
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Fast and Reliable Gradients for Deformables Across Frictional Contact Regimes
The authors develop a differentiable simulator enforcing Markovian dynamics on a position-velocity manifold and using a mass-aligned preconditioner with a soft Fischer-Burmeister operator to produce stable gradients for frictional contact in large-deformation scenarios.