5 papers
Conditional Flow Matching for ML-Based Inverse Design Problems
Juliana Felder, Milad Habibi, Soheyl Massoudi +1
Engineering inverse design is often limited by the high computational cost of iterative solvers for optimization problems constrained by partial differential equations (PDEs) and b…
Beyond Inference-Time Search: Reinforcement Learning Synthesizes Reusable Solvers
Soheyl Massoudi, Gabriel Apaza, Milad Habibi +1
Large language models (LLMs) typically approach combinatorial optimization as an inference-time procedure, solving each instance separately through sampling, search, or repeated pr…
EngiBench: A Framework for Data-Driven Engineering Design Research
Florian Felten, Gabriel Apaza, Gerhard Bräunlich +9
Engineering design optimization seeks to automatically determine the shapes, topologies, or parameters of components that maximize performance under given conditions. This process…
Inverse design with conditional cascaded diffusion models
Milad Habibi, Mark Fuge
Adjoint-based design optimizations are usually computationally expensive and those costs scale with resolution. To address this, researchers have proposed machine learning approach…
Integrating multi-fidelity blood flow data with reduced-order data assimilation
Milad Habibi, Roshan M. D'Souza, Scott T. M. Dawson +1
High-fidelity patient-specific modeling of cardiovascular flows and hemodynamics is challenging. Direct blood flow measurement inside the body with in-vivo measurement modalities s…