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Creators/Authors contains: "Cao, Yadi"

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  1. ; ; ; ; ; ; ; ; (, NeurIPS 2024)
    Free, publicly-accessible full text available December 13, 2025
  2. ; ; ; ; ; ; ; ; (, NeurIPS 2024)
    Free, publicly-accessible full text available December 11, 2025
  3. ; ; ; ; ; ; ; ; (, NeurIPS 2024)
    Free, publicly-accessible full text available December 9, 2025
  4. ; ; ; ; ; ; (, ACM Transactions on Graphics)
    This study presents a new method for modeling the interaction between compressible flow, shock waves, and deformable structures, emphasizing destructive dynamics. Extending advances in time-splitting compressible flow and the Material Point Methods (MPM), we develop a hybrid Eulerian and Lagrangian/Eulerian scheme for monolithic flow-structure interactions. We adopt the second-order WENO scheme to advance the continuity equation. To stably resolve deforming boundaries with sub-cell particles, we propose a blending treatment of reflective and passable boundary conditions inspired by the theory of porous media. The strongly coupled velocity-pressure system is discretized with a new mixed-order finite element formulation employing B-spline shape functions. Shock wave propagation, temperature/density-induced buoyancy effects, and topology changes in solids are unitedly captured. 
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    Full Text Available 
  5. ; ; ; ; ; (, Advances in Neural Information Processing Systems)
    In this paper, we propose a neural network-based approach for learning to represent the behavior of plastic solid materials ranging from rubber and metal to sand and snow. Unlike elastic forces such as spring forces, these plastic forces do not result from the positional gradient of any potential energy, imposing great challenges on the stability and flexibility of their simulation. Our method effectively resolves this issue by learning a generalizable plastic energy whose derivative closely matches the analytical behavior of plastic forces. Our method, for the first time, enables the simulation of a wide range of arbitrary elasticity-plasticity combinations using time step-independent, unconditionally stable optimization-based time integrators. We demonstrate the efficacy of our method by learning and producing challenging 2D and 3D effects of metal, sand, and snow with complex dynamics. 
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    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; (, Neural Information Processing Systems (NeurIPS). 2022.)
    Accepted Manuscript Full Text Available