A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables

• URL: http://arxiv.org/abs/2311.11343v3
• Date: Tue, 21 May 2024 14:01:23 GMT
• Title: A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables
• Authors: Sébastien Bompas, Stefan Sandfeld,
• Abstract summary: In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures. Using generative machine learning models can be a viable solution which also reduces the computational cost. This comes with new challenges because, e.g., a continuous property variable as conditioning input to the model is required. We investigate the shortcomings of an existing method and compare this to a novel embedding strategy for generative models that is based on the binary representation of floating point numbers. This eliminates the need for normalization, preserves information, and creates a versatile embedding space for conditioning the generative model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures. Additionally, finding microstructures for given properties is typically an ill-posed problem where multiple solutions may exist. Using generative machine learning models can be a viable solution which also reduces the computational cost. This comes with new challenges because, e.g., a continuous property variable as conditioning input to the model is required. We investigate the shortcomings of an existing method and compare this to a novel embedding strategy for generative models that is based on the binary representation of floating point numbers. This eliminates the need for normalization, preserves information, and creates a versatile embedding space for conditioning the generative model. This technique can be applied to condition a network on any number, to provide fine control over generated microstructure images, thereby contributing to accelerated materials design.

Related papers

• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Evaluating the diversity and utility of materials proposed by generative models [38.85523285991743]
  We show how one state-of-the-art generative model, the physics-guided crystal generation model, can be used as part of the inverse design process. Our findings suggest how generative models might be improved to enable better inverse design.
  arXiv  Detail & Related papers  (2023-08-09T14:42:08Z)
• Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC [106.06185677214353]
  diffusion models have quickly become the prevailing approach to generative modeling in many domains. We propose an energy-based parameterization of diffusion models which enables the use of new compositional operators. We find these samplers lead to notable improvements in compositional generation across a wide set of problems.
  arXiv  Detail & Related papers  (2023-02-22T18:48:46Z)
• Scientific Machine Learning for Modeling and Simulating Complex Fluids [0.0]
  rheological equations relate internal stresses and deformations in complex fluids. Data-driven models provide accessible alternatives to expensive first-principles models. Development of similar models for complex fluids has lagged.
  arXiv  Detail & Related papers  (2022-10-10T04:35:31Z)
• Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling [3.9181541460605116]
  We propose to use data-driven modeling to predict a material's response without using conventional models. The material response is modeled by learning the implicit mappings between loading conditions and the resultant displacement and/or damage fields. We demonstrate the performance of our proposed method for a number of examples, including hyperelastic, anisotropic and brittle materials.
  arXiv  Detail & Related papers  (2022-03-15T19:08:13Z)
• Low-Rank Constraints for Fast Inference in Structured Models [110.38427965904266]
  This work demonstrates a simple approach to reduce the computational and memory complexity of a large class of structured models. Experiments with neural parameterized structured models for language modeling, polyphonic music modeling, unsupervised grammar induction, and video modeling show that our approach matches the accuracy of standard models at large state spaces.
  arXiv  Detail & Related papers  (2022-01-08T00:47:50Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Conditional Generative Modeling via Learning the Latent Space [54.620761775441046]
  We propose a novel framework for conditional generation in multimodal spaces. It uses latent variables to model generalizable learning patterns. At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes.
  arXiv  Detail & Related papers  (2020-10-07T03:11:34Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.