7 papers
GraIP: A Benchmarking Framework For Neural Graph Inverse Problems
Semih Cantürk, Andrei Manolache, Arman Mielke +5
A wide range of graph learning tasks, such as structure discovery, temporal graph analysis, and combinatorial optimization, focus on inferring graph structures from data, rather th…
Learning (Approximately) Equivariant Networks via Constrained Optimization
Andrei Manolache, Luiz F. O. Chamon, Mathias Niepert
Equivariant neural networks are designed to respect symmetries through their architecture, boosting generalization and sample efficiency when those symmetries are present in the da…
CALM-PDE: Continuous and Adaptive Convolutions for Latent Space Modeling of Time-dependent PDEs
Jan Hagnberger, Daniel Musekamp, Mathias Niepert
Solving time-dependent Partial Differential Equations (PDEs) using a densely discretized spatial domain is a fundamental problem in various scientific and engineering disciplines,…
LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators
Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert
Modeling high-frequency information is a critical challenge in scientific machine learning. For instance, fully turbulent flow simulations of the Navier-Stokes equations at Reynold…
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization
Arman Mielke, Uwe Bauknecht, Thilo Strauss +1
Combinatorial optimization (CO) problems arise across a broad spectrum of domains, including medicine, logistics, and manufacturing. While exact solutions are often computationally…
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion
Vinh Tong, Hoang Trung-Dung, Anji Liu +2
In domains such as molecular and protein generation, physical systems exhibit inherent symmetries that are critical to model. Two main strategies have emerged for learning invarian…