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With the increase in computational power for the available hardware, the demand for high-resolution data in computer graphics applications increases. Consequently, classical geometry processing techniques based on linear algebra solutions…

Graphics · Computer Science 2024-10-08 Filippo Maggioli , Daniele Baieri , Zorah Lähner , Simone Melzi

Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult…

This work presents an optimization framework for tailoring the nonlinear dynamic response of lightly damped mechanical systems using Spectral Submanifold (SSM) reduction. We derive the SSM-based backbone curve and its sensitivity with…

Optimization and Control · Mathematics 2025-12-23 Matteo Pozzi , Jacopo Marconi , Shobhit Jain , Mingwu Li , Francesco Braghin

When a numerical simulation has to handle a physics problem with a wide range of time-dependent length scales, dynamically adaptive discretizations can be the method of choice. We present a major upgrade to the numerical relativity code…

General Relativity and Quantum Cosmology · Physics 2023-05-31 Sarah Renkhoff , Daniela Cors , David Hilditch , Bernd Brügmann

The high computational demands of multiscale modeling necessitate advanced parallel and adaptive strategies. To address this challenge, we introduce an adaptive method that utilizes two microscale models based on an offline database for…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-07-02 Sion Kim , Ezra Kissel , Karel Matous

This paper presents a heterogeneous adaptive mesh refinement (AMR) framework for efficient simulation of moderately stiff reactive problems. This framework features an elaborate subcycling-in-time algorithm along with a specialized…

Computational Physics · Physics 2025-06-04 Yuqi Wang , Yadong Zeng , Ralf Deiterding , Jianhan Liang

Neural Radiance Field (NeRF) and its variants have recently emerged as successful methods for novel view synthesis and 3D scene reconstruction. However, most current NeRF models either achieve high accuracy using large model sizes, or…

Computer Vision and Pattern Recognition · Computer Science 2024-10-16 Shiran Yuan , Hao Zhao

The cost and accuracy of simulating complex physical systems using the Finite Element Method (FEM) scales with the resolution of the underlying mesh. Adaptive meshes improve computational efficiency by refining resolution in critical…

Gravitational instabilities naturally give rise to multi-scale structure, which is difficult for traditional Eulerian hydrodynamic methods to accurately evolve. This can be circumvented by adaptively adding resolution (in the form of…

Astrophysics · Physics 2007-05-23 G. L. Bryan , M. L. Norman

Many engineering systems require accurate simulations of complex physical systems. Yet, analytical solutions are only available for simple problems, necessitating numerical approximations such as the Finite Element Method (FEM). The cost…

Shape optimization is of great significance in structural engineering, as an efficient geometry leads to better performance of structures. However, the application of gradient-based shape optimization for structural and architectural design…

Mathematical Software · Computer Science 2023-06-30 Gaoyuan Wu

Recent point-based differentiable rendering techniques have achieved significant success in high-fidelity reconstruction and fast rendering. However, due to the unstructured nature of point-based representations, they are difficult to apply…

Graphics · Computer Science 2025-08-12 Kaiwen Song , Jinkai Cui , Zherui Qiu , Juyong Zhang

Structured Adaptive Mesh Refinement (SAMR) is a popular numerical technique to study processes with high spatial and temporal dynamic range. It reduces computational requirements by adapting the lattice on which the underlying differential…

Instrumentation and Methods for Astrophysics · Physics 2015-06-12 Ralf Kaehler , Tom Abel

This paper develops an adaptive proximal alternating direction method of multipliers (ADMM) for solving linearly constrained, composite optimization problems under the assumption that the smooth component of the objective is weakly convex,…

Optimization and Control · Mathematics 2026-05-04 Leandro Farias Maia , David H. Gutman , Renato D. C. Monteiro , Gilson N. Silva

Current Adaptive Mesh Refinement (AMR) simulations require algorithms that are highly parallelized and manage memory efficiently. As compute engines grow larger, AMR simulations will require algorithms that achieve new levels of efficient…

Solar and Stellar Astrophysics · Physics 2015-03-19 Jonathan J. Carroll-Nellenback , Brandon Shroyer , Adam Frank , Chen Ding

Many solid mechanics problems on complex geometries are conventionally solved using discrete boundary methods. However, such an approach can be cumbersome for problems involving evolving domain boundaries due to the need to track boundaries…

Numerical Analysis · Mathematics 2025-06-06 Vinamra Agrawal , Brandon Runnels

We consider within a finite element approach the usage of different adaptively refined meshes for different variables in systems of nonlinear, time-depended PDEs. To resolve different solution behaviours of these variables, the meshes can…

Numerical Analysis · Mathematics 2010-05-27 Thomas Witkowski , Axel Voigt

We study the evolution of solidification microstructures using a phase-field model computed on an adaptive, finite element grid. We discuss the details of our algorithm and show that it greatly reduces the computational cost of solving the…

Materials Science · Physics 2009-10-31 Nikolas Provatas , Nigel Goldenfeld , Jonathan Dantzig

Over the last decade block-structured adaptive mesh refinement (SAMR) has found increasing use in large, publicly available codes and frameworks. SAMR frameworks have evolved along different paths. Some have stayed focused on specific…

State-of-the-art differentially private synthetic tabular data has been defined by adaptive 'select-measure-generate' frameworks, exemplified by methods like AIM. These approaches iteratively measure low-order noisy marginals and fit…

Machine Learning · Computer Science 2025-11-14 Samuel Maddock , Shripad Gade , Graham Cormode , Will Bullock