4 citations · 13 across the 20 of their papers we have counts for
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Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models
Tanja Bien, Samir Darouich, Lluís Pastor-Pérez +3
Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated samples. However, improving con…
Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers
Luis Medrano-Navarro, Giacomo Baldan, Qiang Liu +4
Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high cost of generating large-scale…
Learned End-to-End Guidance Schedules for Diffusion Models
Aneesh Barthakur, Mathias Niepert, Luiz F. O. Chamon
Diffusion models are a powerful generative paradigm used across multimedia and scientific applications. Guided diffusion methods impose requirements on the generation by adding the…
Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting
Loay Mualem, Lluís Pastor-Pérez, Vinh Tong +4
Supervised fine-tuning of discrete diffusion language models masks some response tokens and trains the model to recover their original values from the visible context. The masking…
Parallel Tempering for Diffusion-Based Combinatorial Optimization
Arman Mielke, Uwe Bauknecht, Thilo Strauss +1
Discrete diffusion models have emerged as a powerful paradigm for solving combinatorial optimization (CO) problems on graphs by learning to sample high-quality solutions. A common…
Equivariance Breaks the Learning Rate
Andrei Manolache, Mathias Niepert
Equivariant networks are commonly trained with Adam, yet recent work reports that matrix structured optimizers such as Muon can perform better, with the reasons for these gains onl…
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…
ARIA: Adaptive Region-Based Importance Allocation for Conditional Diffusion Distillation
Loay Mualem, Vinh Tong, Samir Darouich +1
Distilling conditional diffusion models aims to transfer the behavior of a large teacher to a smaller student while preserving alignment across conditioning inputs. Unlike recognit…
SymDrift: One-Shot Generative Modeling under Symmetries
Samir Darouich, Vinh Tong, Lluís Pastor-Pérez +3
Generative modeling of physical systems, such as molecules, requires learning distributions that are invariant under global symmetries, such as rotations in three-dimensional space…
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…
SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model
Jan Hagnberger, Mathias Niepert
Machine learning-based surrogate models have emerged as more efficient alternatives to numerical solvers for physical simulations over complex geometries, such as car bodies. Many…