15 citations · 20 across the 9 of their papers we have counts for
10 papers
Gauss-Newton Natural Gradient Descent for Shape Learning
James King, Arturs Berzins, Siddhartha Mishra +1
We explore the use of the Gauss-Newton method for optimization in shape learning, including implicit neural surfaces and geometry-informed neural networks. The method addresses key…
Meltdown: Circuits and Bifurcations in Point-Cloud-Conditioned 3D Diffusion Transformers
Maximilian Plattner, Fabian Paischer, Johannes Brandstetter +1
Sparse point clouds are a common input modality for 3D surface reconstruction, including in safety-critical settings such as surgical navigation and autonomous perception. Recent p…
Neural surrogates for designing gravitational wave detectors
Carlos Ruiz-Gonzalez, Sören Arlt, Sebastian Lehner +5
Physics simulators are essential in science and engineering, enabling the analysis, control, and design of complex systems. In experimental sciences, they are increasingly used to…
Einstein Fields: A Neural Perspective To Computational General Relativity
Sandeep Suresh Cranganore, Andrei Bodnar, Arturs Berzins +1
We introduce Einstein Fields, a neural representation designed to compress computationally intensive four-dimensional numerical relativity simulations into compact implicit neural…
Shape Generation via Weight Space Learning
Maximilian Plattner, Arturs Berzins, Johannes Brandstetter
Foundation models for 3D shape generation have recently shown a remarkable capacity to encode rich geometric priors across both global and local dimensions. However, leveraging the…
Diverse Topology Optimization using Modulated Neural Fields
Andreas Radler, Eric Volkmann, Johannes Brandstetter +1
Topology optimization (TO) is a family of computational methods that derive near-optimal geometries from formal problem descriptions. Despite their success, established TO methods…
Geometry-Informed Neural Networks
Arturs Berzins, Andreas Radler, Eric Volkmann +3
Geometry is a ubiquitous tool in computer graphics, design, and engineering. However, the lack of large shape datasets limits the application of state-of-the-art supervised learnin…
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
Arturs Berzins
A neural network consisting of piecewise affine building blocks, such as fully-connected layers and ReLU activations, is itself a piecewise affine function supported on a polyhedra…
Neural Implicit Shape Editing using Boundary Sensitivity
Arturs Berzins, Moritz Ibing, Leif Kobbelt
Neural fields are receiving increased attention as a geometric representation due to their ability to compactly store detailed and smooth shapes and easily undergo topological chan…
Standardized Non-Intrusive Reduced Order Modeling Using Different Regression Models With Application to Complex Flow Problems
Artūrs Bērziņš, Jan Helmig, Fabian Key +1
In recent years, numerical methods in industrial applications have evolved from a pure predictive tool towards a means for optimization and control. Since standard numerical analys…