# Compressive sensing as a new paradigm for model building

@inproceedings{Nelson2012CompressiveSA, title={Compressive sensing as a new paradigm for model building}, author={Lance J. Nelson and Fei Zhou and Gus L. W. Hart and Vidvuds Ozoliņ{\vs}}, year={2012} }

The widely-accepted intuition that the important properties of solids are determined by a few key variables underpins many methods in physics. Though this reductionist paradigm is applicable in many physical problems, its utility can be limited because the intuition for identifying the key variables often does not exist or is difficult to develop. Machine learning algorithms (genetic programming, neural networks, Bayesian methods, etc.) attempt to eliminate the a priori need for such intuition…

## 26 Citations

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

- Computer Science, PhysicsJ. Comput. Phys.
- 2020

A new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, is described and its usefulness is showcased by providing an example from modeling fracture propagation.

High-throughput prediction of the carrier relaxation time via data-driven descriptor

- Materials Sciencenpj Computational Materials
- 2020

It has been demonstrated that many promising thermoelectric materials, such as tetradymite compounds are also three-dimensional topological insulators. In both cases, a fundamental question is the…

Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications

- Computational Materials Science
- 2019

Abstract Typically, thousands of computationally expensive micro-scale simulations of brittle crack propagation are needed to upscale lower length scale phenomena to the macro-continuum scale.…

Robust data-driven approach for predicting the configurational energy of high entropy alloys

- Materials Science, Physics
- 2019

High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of…

Sparse low rank approximation of potential energy surfaces with applications in estimation of anharmonic zero point energies and frequencies

- Mathematics, ChemistryJournal of Mathematical Chemistry
- 2019

We propose a method that exploits sparse representation of potential energy surfaces (PES) on a polynomial basis set selected by compressed sensing. The method is useful for studies involving large…

Machine learning for interatomic potential models.

- Medicine, Computer ScienceThe Journal of chemical physics
- 2020

An overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression are included.

Bayesian inference of atomistic structure in functional materials

- Computer Science, Materials Sciencenpj Computational Materials
- 2019

A ‘building block’-based Bayesian Optimisation Structure Search (BOSS) approach for addressing extended organic/inorganic interface problems is proposed and its feasibility in a molecular surface adsorption study is demonstrated.

Atomic scale modeling of ordering phenomena in inorganic clathrates

- Materials Science
- 2018

Ordering phenomena in materials often have a crucial impact on materials properties. They are governed by the competition between entropy and energy. Accordingly simulating these aspects requires the…

Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation.

- Materials Science, MedicineThe Journal of chemical physics
- 2018

Genarris is a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints and offers three workflows based on different sequences of successive clustering and selection steps, which are recommended for generating initial populations for genetic algorithms.

Learning physics by data for the motion of a sphere falling in a non-Newtonian fluid

- Computer Science, PhysicsCommun. Nonlinear Sci. Numer. Simul.
- 2019

This model successfully simulates the sustaining oscillations and abrupt increase during the sedimentation of a sphere through a non-Newtonian fluid and presents the behavior of a chaotic system which is highly sensitive to initial conditions.