scholarly journals Combinatorial alloying improves bismuth vanadate photoanodes via reduced monoclinic distortion

2018 ◽  
Vol 11 (9) ◽  
pp. 2444-2457 ◽  
Author(s):  
P. F. Newhouse ◽  
D. Guevarra ◽  
M. Umehara ◽  
S. E. Reyes-Lillo ◽  
L. Zhou ◽  
...  
Keyword(s):  
Solar Fuels ◽  
Bismuth Vanadate ◽  
High Dimensional ◽  
Dimensional Parameter ◽  
Parameter Spaces ◽  
Energy Technologies ◽  
Monoclinic Distortion

Energy technologies are enabled by materials innovations, requiring efficient methods to search high dimensional parameter spaces, such as multi-element alloying for enhancing solar fuels photoanodes.

scholarly journals Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

2021 ◽  
Vol 81 (12) ◽  
Author(s):  
Jacob Hollingsworth ◽  
Michael Ratz ◽  
Philip Tanedo ◽  
Daniel Whiteson
Keyword(s):  
Dimensional Space ◽  
Generative Models ◽  
Boson Mass ◽  
High Dimensional ◽  
Beyond The Standard Model ◽  
Dimensional Parameter ◽  
Parameter Spaces ◽  
Alternative Approach ◽  
The Standard Model ◽  
Standard Models

AbstractModels of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental results project onto a subspace of parameters that are consistent with those observations, but mapping these constraints to the underlying parameters is also typically intractable. Instead, physicists often resort to scanning small subsets of the full parameter space and testing for experimental consistency. We propose an alternative approach that uses generative models to significantly improve the computational efficiency of sampling high-dimensional parameter spaces. To demonstrate this, we sample the constrained and phenomenological Minimal Supersymmetric Standard Models subject to the requirement that the sampled points are consistent with the measured Higgs boson mass. Our method achieves orders of magnitude improvements in sampling efficiency compared to a brute force search.

Pattern selection in high-dimensional parameter spaces

2012 ◽  
Author(s):  
Georg Viehoever ◽  
Brian Ward ◽  
Hans-Juergen Stock
Keyword(s):  
High Dimensional ◽  
Pattern Selection ◽  
Dimensional Parameter ◽  
Parameter Spaces

scholarly journals Efficient characterization of high-dimensional parameter spaces for systems biology

2011 ◽  
Vol 5 (1) ◽  
pp. 142 ◽  
Author(s):  
Elías Zamora-Sillero ◽  
Marc Hafner ◽  
Ariane Ibig ◽  
Joerg Stelling ◽  
Andreas Wagner
Keyword(s):  
Systems Biology ◽  
High Dimensional ◽  
Dimensional Parameter ◽  
Parameter Spaces

scholarly journals Constraining the parameters of high-dimensional models with active learning

2019 ◽  
Vol 79 (11) ◽  
Author(s):  
Sascha Caron ◽  
Tom Heskes ◽  
Sydney Otten ◽  
Bob Stienen
Keyword(s):  
Active Learning ◽  
Particle Physics ◽  
High Energy Physics ◽  
High Energy ◽  
Physical Models ◽  
High Dimensional ◽  
Physical Parameters ◽  
Model Parameters ◽  
Dimensional Parameter ◽  
Parameter Spaces

AbstractConstraining the parameters of physical models with $$>5-10$$>5-10 parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of computational resources. The commonly used solution of reducing the number of relevant physical parameters hampers the generality of the results. In this paper we show that this problem can be alleviated by the use of active learning. We illustrate this with examples from high energy physics, a field where simulations are often expensive and parameter spaces are high-dimensional. We show that the active learning techniques query-by-committee and query-by-dropout-committee allow for the identification of model points in interesting regions of high-dimensional parameter spaces (e.g. around decision boundaries). This makes it possible to constrain model parameters more efficiently than is currently done with the most common sampling algorithms and to train better performing machine learning models on the same amount of data. Code implementing the experiments in this paper can be found on GitHub "Image missing"

scholarly journals Navigator function for the conformal bootstrap

2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Marten Reehorst ◽  
Slava Rychkov ◽  
David Simmons-Duffin ◽  
Benoit Sirois ◽  
Ning Su ◽  
...  
Keyword(s):  
Parameter Space ◽  
Optimization Methods ◽  
High Dimensional ◽  
Dimensional Parameter ◽  
Parameter Spaces ◽  
Standard Methods ◽  
Conformal Bootstrap ◽  
Bootstrap Techniques ◽  
Binary Information ◽  
Quasi Newton

Current numerical conformal bootstrap techniques carve out islands in theory space by repeatedly checking whether points are allowed or excluded. We propose a new method for searching theory space that replaces the binary information "allowed"/"excluded" with a continuous "navigator" function that is negative in the allowed region and positive in the excluded region. Such a navigator function allows one to efficiently explore high-dimensional parameter spaces and smoothly sail towards any islands they may contain. The specific functions we introduce have several attractive features: they are well-defined in large regions of parameter space, can be computed with standard methods, and evaluation of their gradient is immediate due to an SDP gradient formula that we provide. The latter property allows for the use of efficient quasi-Newton optimization methods, which we illustrate by navigating towards the 3d Ising island.

Sign in / Sign up

Export Citation Format

Share Document