Peter Yichen Chen

Orcid: 0000-0003-1919-5437

According to our database1, Peter Yichen Chen authored at least 15 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Model reduction for the material point method via an implicit neural representation of the deformation map.
J. Comput. Phys., April, 2023

How Can Large Language Models Help Humans in Design and Manufacturing?
CoRR, 2023

Learning Preconditioner for Conjugate Gradient PDE Solvers.
CoRR, 2023

Neural Stress Fields for Reduced-order Elastoplasticity and Fracture.
Proceedings of the SIGGRAPH Asia 2023 Conference Papers, 2023

LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields.
Proceedings of the SIGGRAPH Asia 2023 Conference Papers, 2023

Learning Preconditioners for Conjugate Gradient PDE Solvers.
Proceedings of the International Conference on Machine Learning, 2023

Implicit Neural Spatial Representations for Time-dependent PDEs.
Proceedings of the International Conference on Machine Learning, 2023

Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics.
Proceedings of the International Conference on Machine Learning, 2023

PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Multiscaling and Machine Learning Approaches to Physics Simulation
PhD thesis, 2022

CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations.
CoRR, 2022

2021
Model reduction for the material point method via learning the deformation map and its spatial-temporal gradients.
CoRR, 2021

Model Reduction for the Material Point Method on Nonlinear Manifolds Using Deep Learning.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

2018
Hybrid grains: adaptive coupling of discrete and continuum simulations of granular media.
ACM Trans. Graph., 2018


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