Search Results for author: Eitan Grinspun

Found 7 papers, 1 papers with code

Implicit Neural Spatial Representations for Time-dependent PDEs

no code implementations30 Sep 2022 Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen

Whereas classical solvers can dynamically adapt their spatial representation only by resorting to complex remeshing algorithms, our INSR approach is intrinsically adaptive.

Contact mechanics

CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations

no code implementations6 Jun 2022 Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G A Pershing, Henrique Teles Maia, Maurizio M. Chiaramonte, Kevin Carlberg, Eitan Grinspun

We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations.

Model reduction for the material point method via an implicit neural representation of the deformation map

no code implementations25 Sep 2021 Peter Yichen Chen, Maurizio M. Chiaramonte, Eitan Grinspun, Kevin Carlberg

Our technique approximates the $\textit{kinematics}$ by approximating the deformation map using an implicit neural representation that restricts deformation trajectories to reside on a low-dimensional manifold.


Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel

no code implementations15 Sep 2021 Henrique Teles Maia, Chang Xiao, DIngzeyu Li, Eitan Grinspun, Changxi Zheng

We find that each layer component's evaluation produces an identifiable magnetic signal signature, from which layer topology, width, function type, and sequence order can be inferred using a suitably trained classifier and a joint consistency optimization based on integer programming.

Addressing Troubles with Double Bubbles: Convergence and Stability at Multi-Bubble Junctions

1 code implementation14 Oct 2019 Yun, Fei, Christopher Batty, Eitan Grinspun

In this report we discuss and propose a correction to a convergence and stability issue occurring in the work of Da et al.[2015], in which they proposed a numerical model to simulate soap bubbles.

Graphics I.3.7

Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects

no code implementations15 Jul 2016 Yinxiao Li, Yan Wang, Yonghao Yue, Danfei Xu, Michael Case, Shih-Fu Chang, Eitan Grinspun, Peter Allen

A fully featured 3D model of the garment is constructed in real-time and volumetric features are then used to obtain the most similar model in the database to predict the object category and pose.

Object Pose Estimation +1

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