7 papers
AnchorGen: Anchored Optimization for Customizable Generative 3D Design
Hantao Zhang, Oliver Heinimann, Jieke Wu +3
Engineering design often starts from a 2D sketch that fixes style and proportions, yet the subsequent 3D shape optimization relies on learned generative priors to keep the geometry…
Aerodynamic Shape Design Space Exploration with Deep Latent Diffusion Model
Zhen Wei, Edouard Dufour, Colin Pelletier +2
We propose DiffGeo, a latent space diffusion-based generative framework for aerodynamic design space exploration under extreme data scarcity. DiffGeo combines a learned latent spac…
Generative Refinement for Low-Budget Black-Box Optimization
Edouard R. Dufour, Pascal Fua, Oliver Heinimann
Black-box optimization is a fundamental tool in science and engineering for optimizing objectives when gradient information is unavailable. It becomes especially difficult when the…
Neural-Surrogate Co-Design of Aerodynamic Shape and Control
Daniele Affinita, Mingda Xu, Benoît Valentin Gherardi +1
Control co-design jointly optimizes a robot's physical design and its controller. Existing techniques are limited in the number of model parameters and the complexity of the physic…
Annealed Co-Generation: Disentangling Variables via Progressive Pairwise Modeling
Hantao Zhang, Jieke Wu, Mingda Xu +3
For multivariate co-generation in scientific applications, we advocate pairwise block rather than joint modeling of all variables. This design mitigates the computational burden an…
Dflow-SUR: Enhancing Generative Aerodynamic Inverse Design using Differentiation Throughout Flow Matching
Aobo Yang, Zhen Wei, Rhea Liem +1
Generative inverse design requires incorporating physical constraints to ensure that generated designs are both reliable and accurate. However, we observe that current state-of-the…
Do you understand epistemic uncertainty? Think again! Rigorous frequentist epistemic uncertainty estimation in regression
Enrico Foglia, Benjamin Bobbia, Nikita Durasov +4
Quantifying model uncertainty is critical for understanding prediction reliability, yet distinguishing between aleatoric and epistemic uncertainty remains challenging. We extend re…