Tootfinder

A Meshing Framework for Digital Twins for Extrusion based Additive Manufacturing
Lucas Gallup, Kevin N. Long, Devin J. Roach, William D. Reinholtz, Adam Cook, Craig M. Hamel
https://arxiv.org/abs/2509.12436

A Meshing Framework for Digital Twins for Extrusion based Additive Manufacturing
Additive manufacturing (AM) allows for manufacturing of complex three-dimensional geometries not typically realizable with standard subtractive manufacturing practices. The internal microstructure of a 3D printed component can have a significant impact on its mechanical, vibrational, and shock properties and allows for a richer design space when this is controllable. Due to the complex interactions of the internal geometry of an extrusion-based AM component, it is common practice to assume a ho…

Analysis-Aware Defeaturing of Dirichlet Features
Philipp Weder, Annalisa Buffa
https://arxiv.org/abs/2508.13886 https://arxiv.org/pdf/2508.13886

Analysis-Aware Defeaturing of Dirichlet Features
Feature removal from computational geometries, or defeaturing, is an integral part of industrial simulation pipelines. Defeaturing simplifies the otherwise costly or even impossible meshing process, speeds up the simulation, and lowers its memory footprint. Current defeaturing operators are often based on heuristic criteria and ignore the impact of the simplifications on the PDE solution. This work extends the mathematically rigorous framework developed by Buffa, Chanon, and Vázquez (2022) to …

Mesh Processing Non-Meshes via Neural Displacement Fields
Yuta Noma, Zhecheng Wang, Chenxi Liu, Karan Singh, Alec Jacobson
https://arxiv.org/abs/2508.12179 https://

Mesh Processing Non-Meshes via Neural Displacement Fields
Mesh processing pipelines are mature, but adapting them to newer non-mesh surface representations -- which enable fast rendering with compact file size -- requires costly meshing or transmitting bulky meshes, negating their core benefits for streaming applications. We present a compact neural field that enables common geometry processing tasks across diverse surface representations. Given an input surface, our method learns a neural map from its coarse mesh approximation to the surface. The f…

An Improved Physically-Based Surface Triangulation Method
Lei Shangyu, Fan Wei, Ren Hui
https://arxiv.org/abs/2508.05099 https://arxiv.org/pdf/2508.05099…

An Improved Physically-Based Surface Triangulation Method
This paper proposes improvements to the physically-based surface triangulation method, bubble meshing. The method simulates physical bubbles to automatically generate mesh vertices, resulting in high-quality Delaunay triangles. Despite its flexibility in local mesh size control and the advantage of local re-meshing, bubble meshing is constrained by high computational costs and slow convergence on complex surfaces. The proposed approach employs conformal mapping to simplify surface bubble packin…

The full frequency sweep won't finish before I leave for REcon but I think I've finally got the ARF6 launch dialed in: better than -20 dB return loss from DC to 11.9 GHz, and better than -15.8 dB from DC to 30 GHz.
* Shrink vias from 0.45 pad / 0.25 drill to 0.35 / 0.15
* Use round vias for the signal lines as coupling seemed to be significant vs the square ones I tried to use to simplify meshing
* Keep non-functional via pads
* Single large ground plane cutout o…

My daughter's softball team this summer season is something else. For some reason this group of girls who for the most part didn't know each other from other leagues or seasons, are meshing like crazy.
Stuff like asking for extra practice, lol. Usually it's 2 hours, and they ask to practice for one more hour. Constantly asking to pitch to them hitting.
Today they spent 4 hours at a community pool, just playing. Very supportive to each other, etc. Amazing to watch.

PaMO: Parallel Mesh Optimization for Intersection-Free Low-Poly Modeling on the GPU
Seonghun Oh, Xiaodi Yuan, Xinyue Wei, Ruoxi Shi, Fanbo Xiang, Minghua Liu, Hao Su
https://arxiv.org/abs/2509.05595

PaMO: Parallel Mesh Optimization for Intersection-Free Low-Poly Modeling on the GPU
Reducing the triangle count in complex 3D models is a basic geometry preprocessing step in graphics pipelines such as efficient rendering and interactive editing. However, most existing mesh simplification methods exhibit a few issues. Firstly, they often lead to self-intersections during decimation, a major issue for applications such as 3D printing and soft-body simulation. Second, to perform simplification on a mesh in the wild, one would first need to perform re-meshing, which often suffers…

Hierarchical Finite-Element Analysis of Multiscale Electromagnetic Problems via Sparse Operator-Adapted Wavelet Decomposition
F. \c{S}{\i}k, F. L. Teixeira, B. Shanker
https://arxiv.org/abs/2507.17989 …

Hierarchical Finite-Element Analysis of Multiscale Electromagnetic Problems via Sparse Operator-Adapted Wavelet Decomposition
In this paper, we present a finite element method (FEM) framework enhanced by an operator-adapted wavelet decomposition algorithm designed for the efficient analysis of multiscale electromagnetic problems. Usual adaptive FEM approaches, while capable of achieving the desired accuracy without requiring a complete re-meshing of the computational domain, inherently couple different resolution levels. This coupling requires recomputation of coarser-level solutions whenever finer details are added t…

Multi-Order Monte Carlo IMEX hierarchies for uncertainty quantification in multiscale hyperbolic systems
Giulia Bertaglia, Walter Boscheri, Lorenzo Pareschi
https://arxiv.org/abs/2508.20187

Multi-Order Monte Carlo IMEX hierarchies for uncertainty quantification in multiscale hyperbolic systems
We introduce a novel Multi-Order Monte Carlo approach for uncertainty quantification in the context of multiscale time-dependent partial differential equations. The new framework leverages Implicit-Explicit Runge-Kutta time integrators to satisfy the asymptotic-preserving property across different discretization orders of accuracy. In contrast to traditional Multi-Level Monte Carlo methods, which require costly hierarchical re-meshing, our method constructs a multi-order hierarchy by varying bo…

A Physics-Informed Neural Network Framework for Simulating Creep Buckling in Growing Viscoelastic Biological Tissues
Zhongya Lin, Jinshuai Bai, Shuang Li, Xindong Chen, Bo Li, Xi-Qiao Feng
https://arxiv.org/abs/2506.18565

A Physics-Informed Neural Network Framework for Simulating Creep Buckling in Growing Viscoelastic Biological Tissues
Modeling viscoelastic behavior is crucial in engineering and biomechanics, where materials undergo time-dependent deformations, including stress relaxation, creep buckling and biological tissue development. Traditional numerical methods, like the finite element method, often require explicit meshing, artificial perturbations or embedding customised programs to capture these phenomena, adding computational complexity. In this study, we develop an energy-based physics-informed neural network (PIN…