Results 91 to 100 of about 851,390 (284)
Gaussian processes for inferring parton distributions
Journal of High Energy PhysicsThe extraction of parton distribution functions (PDFs) from experimental or lattice QCD data is an ill-posed inverse problem, where regularization strongly impacts both systematic uncertainties and the reliability of the results.
Yamil Cahuana Medrano +5 more
doaj +1 more source
Sensors, 2020 Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors.
Jonas Beuchert +3 more
doaj +1 more source
Adversarially Robust Optimization with Gaussian Processes [PDF]
, 2018 In this paper, we consider the problem of Gaussian process (GP) optimization with an added robustness requirement: The returned point may be perturbed by an adversary, and we require the function value to remain as high as possible even after this ...
Bogunovic, Ilija +3 more
core +1 more source
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Numerical Solutions of Hamilton-Jacobi Inequalities by Constrained Gaussian Process Regression
SICE Journal of Control, Measurement, and System Integration, 2018 This paper proposes numerical solutions of Hamilton-Jacobi inequalities based on constrained Gaussian process regression. While Gaussian process regression is a tool to estimate an unknown function from its input and output data conventionally, the ...
Kenji Fujimoto +2 more
doaj +1 more source
Machine-Learning Methods for Computational Science and Engineering
Computation, 2020 The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing.
Michael Frank +2 more
doaj +1 more source
Optimality of Poisson processes intensity learning with Gaussian processes [PDF]
, 2015 In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a $d$-dimensional domain. This method was proposed by Adams, Murray and MacKay (ICML,
Kirichenko, Alisa, van Zanten, Harry
core +2 more sources
Near‐Field Electrospinning Micro‐Printhead Achieves Precise Control of Nanofiber Deposition
Advanced Engineering Materials, EarlyView.A micro‐printhead for near‐field electrospinning enables reproducible deposition of polymer nanofibers with diameters below 50 nm. Systematic parameter studies uncover the mechanisms linking operating conditions to fiber morphology, paving the way for precise and low‐cost nanoscale 3D manufacturing.As a high‐resolution, cost‐effective, and rapid ...
Han Xu, Dario Mager, Jan G. Korvink
wiley +1 more source
Gaussian Process Synthesis of Artificial Sounds
Applied Sciences, 2020 In this paper, we propose Gaussian Process (GP) sound synthesis. A GP is used to sample random continuous functions, which are then used for wavetable or waveshaping synthesis.
Aristotelis Hadjakos
doaj +1 more source
Student-t Processes as Alternatives to Gaussian Processes [PDF]
, 2014 We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away ...
Ghahramani, Zoubin +2 more
core +1 more source