5 papers · 1 filter
Distillation of Foundation Models for Time-dependent PDEs
Daniel Musekamp, Boshra Ariguib, Andrei Manolache +1
Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can generalize effectively to new do…
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…
ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
Adrian Catalin Lutu, Ioana Pintilie, Elena Burceanu +1
We present ChronoGraph, a graph-structured multivariate time series forecasting dataset built from real-world production microservices. Each node is a service that emits a multivar…
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…
MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning
Andrei Manolache, Dragos Tantaru, Mathias Niepert
In this work, we propose a simple transformer-based baseline for multimodal molecular representation learning, integrating three distinct modalities: SMILES strings, 2D graph repre…