Preset Based Interaction with High Dimensional Parameter Spaces

2003 ◽  
pp. 391-406 ◽  
Author(s):  
Jarke J. Wijk ◽  
Cornelius W. A. M. Overveld
Keyword(s):  
High Dimensional ◽  
Dimensional Parameter ◽  
Parameter Spaces

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 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.

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 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 High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

2017 ◽  
Author(s):  
Jonathan Ozik ◽  
Nicholson Collier ◽  
Justin M. Wozniak ◽  
Charles Macal ◽  
Chase Cockrell ◽  
...  
Keyword(s):  
Hypothesis Testing ◽  
High Throughput ◽  
Computational Models ◽  
Scale Model ◽  
High Dimensional ◽  
Agent Based Models ◽  
Dimensional Parameter ◽  
Parameter Spaces ◽  
Agent Based ◽  
Early Results

AbstractBackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment’s success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies—one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization—can provide a computational means for high-throughput hypothesis testing, and eventually, optimization.ResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.ConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.

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