Some thoughts on Gaussian processes for emulation of deterministic computer models: Michael Stein
Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain a better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are
From playlist Effective and efficient gaussian processes
(ML 19.1) Gaussian processes - definition and first examples
Definition of a Gaussian process. Elementary examples of Gaussian processes.
From playlist Machine Learning
(ML 19.2) Existence of Gaussian processes
Statement of the theorem on existence of Gaussian processes, and an explanation of what it is saying.
From playlist Machine Learning
(ML 16.7) EM for the Gaussian mixture model (part 1)
Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.
From playlist Machine Learning
(ML 16.8) EM for the Gaussian mixture model (part 2)
Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.
From playlist Machine Learning
(ML 19.3) Examples of Gaussian processes (part 1)
Illustrative examples of several Gaussian processes, and visualization of samples drawn from these Gaussian processes. (Random planes, Brownian motion, squared exponential GP, Ornstein-Uhlenbeck, a periodic GP, and a symmetric GP).
From playlist Machine Learning
Known Boundary Emulation of Complex Computer Models: Ian Vernon, Cambridge
Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain a better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are
From playlist Effective and efficient gaussian processes
Integrated emulator for multi-physics systems of computer models: Deyu Ming, UCL
Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are us
From playlist Effective and efficient gaussian processes
GP Emulators applied to UQ workflows in practice: Eric Daub, Turing
Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain a better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are
From playlist Effective and efficient gaussian processes
Sequential Design based on Mutual Information for Computer Experiments: Joakim Beck, KAUST
Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are us
From playlist Effective and efficient gaussian processes
Accelerating and improving climate models with hybrid AI approaches by Tapio Schneider
DISCUSSION MEETING: WORKSHOP ON CLIMATE STUDIES (HYBRID) ORGANIZERS: Rama Govindarajan (ICTS-TIFR, India), Sandeep Juneja (TIFR, India), Ramalingam Saravanan (Texas A&M University, USA) and Sandip Trivedi (TIFR, India) DATE : 01 March 2022 to 03 March 2022 VENUE: Ramanujan Lecture Hall
From playlist Workshop on Climate Studies - 2022
Are GCMs obsolete? by Venkatramani Balaji
DISCUSSION MEETING: WORKSHOP ON CLIMATE STUDIES (HYBRID) ORGANIZERS: Rama Govindarajan (ICTS-TIFR, India), Sandeep Juneja (TIFR, India), Ramalingam Saravanan (Texas A&M University, USA) and Sandip Trivedi (TIFR, India) DATE : 01 March 2022 to 03 March 2022 VENUE: Ramanujan Lecture Hall
From playlist Workshop on Climate Studies - 2022
Efficient an effective calibration of spatio-temporal models: Dan Williamson & James Salter, Exeter
Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain a better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are
From playlist Effective and efficient gaussian processes
Reconfigurable optical implementation of quantum (...) - V. Parigi - Workshop 1 - CEB T2 2018
Valentina Parigi (Sorbonne Univ., Paris) / 15.05.2018 Reconfigurable optical implementation of quantum complex networks We propose an experimental procedure for the optical implementation of quantum complex networks. The implementation of collections of systems arranged in a network stru
From playlist 2018 - T2 - Measurement and Control of Quantum Systems: Theory and Experiments
PUSHING A GAUSSIAN TO THE LIMIT
Integrating a gaussian is everyones favorite party trick. But it can be used to describe something else. Link to gaussian integral: https://www.youtube.com/watch?v=mcar5MDMd_A Link to my Skype Tutoring site: dotsontutoring.simplybook.me or email dotsontutoring@gmail.com if you have ques
From playlist Math/Derivation Videos
Deep Learning-accelerated cosmological inference from next-generation surveys-Alessio Spurio Mancini
Topic: COSMOPOWER: Deep Learning - accelerated cosmological inference from next-generation surveys. Speaker: Alessio Spurio Mancini Affiliation: University College London Date: May 9, 2022
From playlist IAS/PU Cosmology Discussion