![[2602.15648] Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.15648] Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design
Summary
This paper presents a novel method for inverse material design using guided diffusion and optimized loss functions, addressing challenges in design space structure and gradient-based optimization.
Why It Matters
The research is significant as it introduces a new approach to inverse design problems in engineering and materials science, leveraging diffusion models to enhance the diversity and accuracy of material designs. This could lead to more efficient and innovative material development processes, which are crucial in various industries.
Key Takeaways
- Introduces a guided diffusion method for inverse material design.
- Addresses challenges in gradient-based optimization in discrete design spaces.
- Demonstrates effectiveness in achieving diverse designs with minimal error.
- Utilizes a multi-objective loss function to optimize material density.
- Applicable to both 2D and 3D composite material design problems.
Computer Science > Machine Learning arXiv:2602.15648 (cs) [Submitted on 17 Feb 2026] Title:Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design Authors:Jens U. Kreber, Christian Weißenfels, Joerg Stueckler View a PDF of the paper titled Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design, by Jens U. Kreber and 2 other authors View PDF HTML (experimental) Abstract:Inverse design problems are common in engineering and materials science. The forward direction, i.e., computing output quantities from design parameters, typically requires running a numerical simulation, such as a FEM, as an intermediate step, which is an optimization problem by itself. In many scenarios, several design parameters can lead to the same or similar output values. For such cases, multi-modal probabilistic approaches are advantageous to obtain diverse solutions. A major difficulty in inverse design stems from the structure of the design space, since discrete parameters or further constraints disallow the direct use of gradient-based optimization. To tackle this problem, we propose a novel inverse design method based on diffusion models. Our approach relaxes the original design space into a continuous grid representation, where gradients can be computed by implicit differentiation in the forward simulation. A diffusion model is trained on this relaxed parameter space in order to serve as a prior for plausible relaxed d...