25 papers · ranked by Valyu relevance
Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea + 1 more
'Thomas Pinder'] Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming…
Max Lamparth, Mattis Bestehorn, Bastian Märkisch
High-precision measurements require optimal setups and analysis tools to achieve continuous improvements. Systematic corrections need to be modeled with high accuracy and known uncertainty to reconstruct underlying physical phenomena. To this end, we present Gaussian processes for modeling experiments and usage with…
Fabian Berns, Jan Hüwel, Christian Beecks
Gaussian process models (GPMs) are widely regarded as a prominent tool for learning statistical data models that enable interpolation, regression, and classification. These models are typically instantiated by a Gaussian Process with a zero-mean function and a radial basis covariance function. While these default…
Allen Ross, Jason Lloyd-Price, Ali Rahnavard
Identifying meaningful associations from small-sample longitudinal data is challenging, especially in low signal-to-noise environments where the Gaussian likelihood assumption does not hold. We introduce two methods to algorithmically perform variable selection with sparse, irregularly sampled, longitudinal count data…
Gracie M. White, Amanda P. Siegel, Andres Tovar, Jacek Mateusz Bajkowski + 3 more
The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently…
Parul V. Patil, Robert B. Gramacy, Leah R. Johnson
Ecological time series are often unevenly sampled in time. That is, because the sampling processes used are resource intensive, data may be collected infrequently, or with adaptive frequencies triggered by presence of a target variable. When the data are irregularly spaced, standard time series methods may not be…
Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
— Model Predictive Control evolved as the state of the art paradigm for safety critical control tasks. Controlas-Inference approaches thereof model the constrained optimization problem as a probabilistic inference problem. The constraints have to be implemented into the inference model. A recently introduced…
Parul V. Patil, Robert B. Gramacy, Leah R. Johnson
Ecological time series are often unevenly sampled in time. That is, because the sampling processes used are resource intensive, data may be collected infrequently, or with adaptive frequencies triggered by presence of a target variable. When the data are irregularly spaced, standard time series methods may not be…
Eldon A. Gunn, Nikhil Sengupta, Ben Swallow
software package and GPUs Authors: ['Eldon A. Gunn' 'Nikhil Sengupta' 'Ben Swallow'] Gaussian process are a widely-used statistical tool for conducting non-parametric inference in applied sciences, with many computational packages available to fit to data and predict future observations. We study the use of the Greta…
Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon
Gaussian processes (GPs) are an important tool in machine learning and statistics. However, off-the-shelf GP inference procedures are limited to datasets with several thousand data points because of their cubic computational complexity. For this reason, many sparse GPs techniques have been developed over the past…
Bernardo Fichera, Viacheslav Borovitskiy, Andreas Krause, Aude Billard
'Aude Billard'] Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets. However, it struggles with high-dimensional data. One possible way to scale this technique to higher dimensions is to leverage the implicit…
Roland Preuss, Udo von Toussaint, Frank Nielsen, Frédéric Barbaresco + 2 more
'Ali Mohammad-Djafari' 'Martino Trassinelli'] Data for complex plasma-wall interactions require long-running and expensive computer simulations. Furthermore, the number of input parameters is large, which results in low coverage of the (physical) parameter space. Unpredictable occasions of outliers create a need to…
Juan J. L. Velázquez, Luis A. Escamilla, Purba Mukherjee, J. Alberto Vázquez
Processes Regression Authors: ['Juan J. L. Velázquez' 'Luis A. Escamilla' 'Purba Mukherjee' 'J. Alberto Vázquez'] The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine…
Marius Marinescu, Alberto Olivares, Ernesto Staffetti, Junzi Sun + 1 more
'Chi-Hua Chen'] Wind velocity field knowledge is crucial for the future air traffic management paradigm and is key in many applications, such as aircraft performance studies. This paper addresses the problem of spatio-temporal windc velocity field estimation. The north and east wind components within a given air space…
Samantha Petti, Carlos Martí-Gómez, Justin B. Kinney, Juannan Zhou + 1 more
Mappings from biological sequences (DNA, RNA, protein) to quantitative measures of sequence functionality play an important role in contemporary biology. We are interested in the related tasks of (i) inferring predictive sequence-to-function maps and (ii) decomposing sequence-function maps to elucidate the…
Kanta Matsunaga, Takuto Harada, Shintaro Harada, Akinori Sato + 4 more
Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process Authors: ['Kanta Matsunaga' 'Takuto Harada' 'Shintaro Harada' 'Akinori Sato' 'Shota Terai' 'Mutsunori Uenuma' 'Tomoyuki Miyao' 'Yukiharu Uraoka'] During data-driven process condition optimization on…
Alex Stephens, Matthew Budd, Michal Staniaszek, Benoit Casseau + 4 more
The exploration of new environments is a crucial challenge for mobile robots. This task becomes even more complex with the added requirement of ensuring safety. Here, safety refers to the robot staying in regions where the values of certain environmental conditions (such as terrain steepness or radiation levels) are…
M. Rule, P. Chaudhuri-Vayalambrone, M. Krstulovic, M. Bauza + 2 more
We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid…
Adeleke Maradesa, Baptiste Py, Emanuele Quattrocchi, Francesco Ciucci
Electrochemical impedance spectroscopy (EIS) is a tool widely used to study the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as one of the main methods for the analysis of EIS spectra. Gaussian processes can be used to regress EIS data, quantify uncertainty, and…
Sho Takaoka, Hiromasa Kaneko
Green ammonia is attracting attention as a way of realizing a decarbonized society. In this study, we designed a green ammonia production process under limited power supply. In conventional process optimization, candidates for the design variables are selected based on the experience of the process engineer and…
M. Nicolás Cruz-Bournazou, Harini Narayanan, Alessandro Fagnani, Alessandro Butté
Hybrid modeling, meaning the integration of data-driven and knowledge-based methods, is quickly gaining popularity among many research fields, including bioprocess engineering and development. Recently, the data-driven part of hybrid methods have been largely extended with machine learning algorithms (e.g., artificial…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…