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Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models. Besides enabling scalability, one of their main advantages over sparse…

Machine Learning · Statistics 2021-02-24 Simone Rossi , Markus Heinonen , Edwin V. Bonilla , Zheyang Shen , Maurizio Filippone

We present a multiscale continuous Galerkin (MSCG) method for the fast and accurate stochastic simulation and optimization of time-harmonic wave propagation through photonic crystals. The MSCG method exploits repeated patterns in the…

Numerical Analysis · Mathematics 2020-04-30 Ferran Vidal-Codina , Joel Saa-Seoane , Ngoc-Cuong Nguyen , Jaime Peraire

Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing CG force fields to match all-atom simulations has mainly…

Computational Physics · Physics 2023-02-07 Jonas Köhler , Yaoyi Chen , Andreas Krämer , Cecilia Clementi , Frank Noé

High-throughput characterization often requires estimating parameters and model dimension from experimental data of limited quantity and quality. Such data may result in an ill-posed inverse problem, where multiple sets of parameters and…

Quantum Physics · Physics 2026-04-08 Abigail N. Poteshman , Jiwon Yun , Tim H. Taminiau , Giulia Galli

This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models---including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation---which…

Machine Learning · Computer Science 2014-11-17 Anima Anandkumar , Rong Ge , Daniel Hsu , Sham M. Kakade , Matus Telgarsky

In this paper, a new efficient and generalized consistency correction method for weakly-compressible smoothed particle hydrodynamics is proposed and successfully implemented in the simulation of violent free-surface flow exhibiting breaking…

Fluid Dynamics · Physics 2024-05-24 Yaru Ren , Pengzhi Lin , Chi Zhang , Xiangyu Hu

A systematic Bayesian framework is developed for physics constrained parameter inference ofstochastic differential equations (SDE) from partial observations. The physical constraints arederived for stochastic climate models but are…

Data Analysis, Statistics and Probability · Physics 2016-11-25 Daniel Peavoy , Christian L. E. Franzke , Gareth O. Roberts

Efficiently solving the Fokker-Planck equation (FPE) is crucial for understanding the probabilistic evolution of stochastic particles in dynamical systems, however, analytical solutions or density functions are only attainable in specific…

Computational Physics · Physics 2025-03-13 Xiaolong Wang , Jing Feng , Gege Wang , Tong Li , Yong Xu

This paper proposes a novel low-rank approximation to the multivariate State-Space Model. The Stochastic Partial Differential Equation (SPDE) approach is applied component-wise to the independent-in-time Mat\'ern Gaussian innovation term in…

With the rapid discovery of emergent phenomena in deep learning and large language models, understanding their cause has become an urgent need. Here, we propose a rigorous entropic-force theory for understanding the learning dynamics of…

Machine Learning · Computer Science 2026-02-04 Liu Ziyin , Yizhou Xu , Isaac Chuang

For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare…

High Energy Physics - Phenomenology · Physics 2025-09-03 Joshua Albert , Csaba Balazs , Andrew Fowlie , Will Handley , Nicholas Hunt-Smith , Roberto Ruiz de Austri , Martin White

Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed. While removing this assumption can improve prediction, fitting such models is challenging. In this work,…

Computation · Statistics 2019-05-02 Karla Monterrubio-Gómez , Lassi Roininen , Sara Wade , Theo Damoulas , Mark Girolami

The post-Moore era casts a shadow of uncertainty on many aspects of computer system design. Managing that uncertainty requires new algorithmic tools to make quantitative assessments. While prior uncertainty quantification methods, such as…

Signal Processing · Electrical Eng. & Systems 2019-10-22 Zichang He , Weilong Cui , Chunfeng Cui , Timothy Sherwood , Zheng Zhang

Many engineering systems are subject to spatially distributed uncertainty, i.e. uncertainty that can be modeled as a random field. Altering the mean or covariance of this uncertainty will in general change the statistical distribution of…

Optimization and Control · Mathematics 2014-07-09 Eric Dow , Qiqi Wang

Implicit particle filtering is a sequential Monte Carlo method for data assim- ilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by min- imizing, for…

Numerical Analysis · Mathematics 2015-05-30 Matthias Morzfeld , Alexandre J. Chorin

In recent years, stochastic effects have become increasingly relevant for describing fluid behaviour, particularly in the context of turbulence. The most important model for inviscid fluids in computational fluid dynamics are the Euler…

Numerical Analysis · Mathematics 2024-12-11 Dominic Breit , Thamsanqa Castern Moyo , Philipp Öffner

Gaussian processes (GPs) have gained popularity as flexible machine learning models for regression and function approximation with an in-built method for uncertainty quantification. However, GPs suffer when the amount of training data is…

Machine Learning · Statistics 2025-11-26 Jonas Latz , Aretha L. Teckentrup , Simon Urbainczyk

Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is…

Computation · Statistics 2016-04-18 Andreas Svensson , Arno Solin , Simo Särkkä , Thomas B. Schön

Mechanistic simulation models are inverted against observations in order to gain inference on modeled processes. However, with the increasing ability to collect high resolution observations, these observations represent more patterns of…

Computation · Statistics 2018-12-20 Thomas Wutzler

We propose a unifying view of two different Bayesian inference algorithms, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) and Stein Variational Gradient Descent (SVGD), leading to improved and efficient novel sampling schemes. We…

Machine Learning · Statistics 2020-02-25 Victor Gallego , David Rios Insua
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