Skip to content

Commit

Permalink
add pubs
Browse files Browse the repository at this point in the history
  • Loading branch information
LamWS committed Sep 6, 2024
1 parent bdc5c16 commit ced4057
Show file tree
Hide file tree
Showing 4 changed files with 109 additions and 0 deletions.
Binary file added content/publication/GIPC-2024/featured.png
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
52 changes: 52 additions & 0 deletions content/publication/GIPC-2024/index.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,52 @@
---
# Documentation: https://wowchemy.com/docs/managing-content/

title: 'GIPC: Fast and Stable Gauss-Newton Optimization of IPC Barrier Energy'
subtitle: ''
summary: ''
authors:
- Kemeng Huang
- Floyd M. Chitalu
- Huancheng Lin
- Taku Komura

tags:
- 'IPC'
- 'Barrier Hessian'
- 'Eigen Analysis'
- 'GPU'

categories: []
date: '2024-03-24'
lastmod: 2021-01-15T21:34:50Z
featured: false
draft: false

# Featured image
# To use, add an image named `featured.jpg/png` to your page's folder.
# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
image:
caption: ''
focal_point: ''
preview_only: false

# Projects (optional).
# Associate this post with one or more of your projects.
# Simply enter your project's folder or file name without extension.
# E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
# Otherwise, set `projects = []`.
projects: []
publishDate: '2024-03-24T21:34:50.388741Z'
publication_types:
# 1 Conference paper
# 2 Journal article
# 3 Preprint
# 4 Report
# 5 Book
# 6 Book section
# 7 Thesis
# 8 Patent
- '2'
abstract: Barrier functions are crucial for maintaining an intersection- and inversion-free simulation trajectory but existing methods, which directly use distance can restrict implementation design and performance. We present an approach to rewriting the barrier function for arriving at an efficient and robust approximation of its Hessian. The key idea is to formulate a simplicial geometric measure of contact using mesh boundary elements, from which analytic eigensystems are derived and enhanced with filtering and stiffening terms that ensure robustness with respect to the convergence of a Project-Newton solver. A further advantage of our rewriting of the barrier function is that it naturally caters to the notorious case of nearly parallel edge-edge contacts for which we also present a novel analytic eigensystem. Our approach is thus well suited for standard second-order unconstrained optimization strategies for resolving contacts, minimizing nonlinear nonconvex functions where the Hessian may be indefinite. The efficiency of our eigensystems alone yields a 3× speedup over the standard Incremental Potential Contact (IPC) barrier formulation. We further apply our analytic proxy eigensystems to produce an entirely GPU-based implementation of IPC with significant further acceleration.
publication: 'ACM Transactions on Graphics (TOG)'
---
Binary file added content/publication/aARAP-2024/featured.png
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
57 changes: 57 additions & 0 deletions content/publication/aARAP-2024/index.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,57 @@
---
# Documentation: https://wowchemy.com/docs/managing-content/

title: 'Analytic rotation-invariant modelling of anisotropic finite elements'
subtitle: ''
summary: ''
authors:
- Huancheng Lin
- Floyd M. Chitalu
- Taku Komura

tags:
- 'Finite Elements'
- 'Anisotropy'
- 'Orthotropy'
- 'Physical Simulation'

categories: []
date: '2024-08-09'
lastmod: 2021-01-15T21:34:50Z
featured: false
draft: false

# Featured image
# To use, add an image named `featured.jpg/png` to your page's folder.
# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
image:
caption: ''
focal_point: ''
preview_only: false

# Projects (optional).
# Associate this post with one or more of your projects.
# Simply enter your project's folder or file name without extension.
# E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
# Otherwise, set `projects = []`.
projects: []
publishDate: '2024-08-09T21:34:50.388741Z'
publication_types:
# 1 Conference paper
# 2 Journal article
# 3 Preprint
# 4 Report
# 5 Book
# 6 Book section
# 7 Thesis
# 8 Patent
- '2'
abstract: Anisotropic hyperelastic distortion energies
are used to solve many problems in fields like computer graphics and engineering with applications in shape analysis, deformation, design, mesh parameterization, biomechanics and more.
However, formulating a robust anisotropic energy that is low-order and yet sufficiently non-linear remains a challenging problem for achieving the convergence promised by Newton-type methods in numerical optimization.
In this paper, we propose a novel analytic formulation of an anisotropic energy that is smooth everywhere, low-order, rotationally-invariant and at-least twice differentiable.
At its core, our approach utilizes implicit rotation factorizations with invariants of the Cauchy-Green tensor that arises from the deformation gradient.
The versatility and generality of our analysis is demonstrated through a variety of examples, where we also show that the constitutive law suggested by the anisotropic version of the well-known \textit{As-Rigid-As-Possible} energy is the foundational parametric description of both passive and active elastic materials.
The generality of our approach means that we can systematically derive the force and force-Jacobian expressions for use in implicit and quasistatic numerical optimization schemes, and we can also use our analysis to rewrite, simplify and speedup several existing anisotropic \textit{and} isotropic distortion energies with guaranteed inversion-safety.
publication: 'ACM Transactions on Graphics (TOG)'
---

0 comments on commit ced4057

Please sign in to comment.