STUOD Sandbox Workshops
STUOD Sandbox Workshops
2020
4th Sandbox Workshop | Friday, 30th October | https://www.imperial.ac.uk/events/125441/4th-stuod-sandbox-workshop/ | On examining TGQ | |
3rd Sandbox Workshop | Friday, 4th September | https://www.imperial.ac.uk/events/122286/3rd-stuod-sandbox-workshop/ | On examining Exemplar 2 | |
2nd Sandbox Workshop | Friday, 3rd July | https://www.imperial.ac.uk/events/120517/2nd-stuod-sandbox-workshop/ | On examining Exemplar 1 | |
1st Sandbox Workshop | Friday, 29th May | https://www.imperial.ac.uk/events/119184/first-stuod-sandbox-meeting/ |
2021
12th Sandbox Workshop | Friday, 3 December | https://www.imperial.ac.uk/events/142775/12th-stuod-sandbox-workshop-2/ | On the observation errors |
11th Sandbox Workshop | Friday, 5 November | https://www.imperial.ac.uk/events/141350/11th-stuod-sandbox-workshop/ | On practical parameterisation of the subscale |
10th Sandbox Workshop | Friday, 5 November | https://www.imperial.ac.uk/events/135922/10th-stuod-sandbox-workshop/ | |
9th Sandbox Workshop | Friday, 9 July | https://www.imperial.ac.uk/events/135920/9th-stuod-sandbox-workshop/ | On data coming from model experimental and numerical data |
8th Sandbox Workshop | Friday, 7 May | https://www.imperial.ac.uk/events/133598/8th-sandbox-stuod-sandbox-workshop/ | On examining stochastic partial differential equations (SPDEs) |
7th Sandbox Workshop | Friday, 12 March | https://www.imperial.ac.uk/events/131252/7th-sandbox-stuod-sandbox-workshop/ | On data coming from model experimental and numerical data |
6th Sandbox Workshop | Friday, 12 February | https://www.imperial.ac.uk/events/129403/6th-sandbox-stuod-sandbox-workshop/ | On LU Models |
5th Sandbox Workshop | Friday, 15 January | https://www.imperial.ac.uk/events/128217/5th-stuod-sandbox-workshop/ | On Wave Current Interactions |
2022
18th Sandbox Workshop | Friday, 25 November | https://www.imperial.ac.uk/events/149584/18th-stuod-sandbox-workshop-2/ | On High Resolution Numerical Simulation Data |
17th Sandbox Workshop | Friday, 24 June | https://www.imperial.ac.uk/events/149585/17th-stuod-sandbox-workshop-2-2/ | On Machine Learning |
16th Sandbox Workshop | Friday, 20 May | 16th STUOD Sandbox Workshop | Events | Imperial College London | On Numerics |
15th Sandbox Workshop | Friday, 25 April | 15th STUOD Sandbox Workshop | Events | Imperial College London | On the Analysis of SPDE |
14th Sandbox Workshop | Friday, 25 February | https://www.imperial.ac.uk/events/142822/14th-stuod-sandbox-workshop-2/ | On the collaborative research with the University of Twente |
13th Sandbox Workshop | Friday, 28 January | https://www.imperial.ac.uk/events/142816/13th-stuod-sandbox-workshop/ | On Data Assimilation |
2023
26th Sandbox Workshop | Friday, 8th December |
SCHEDULE 1200-1430 (Hybrid session) Andrew Stuart - Learning Solution Operators For PDEs: Algorithms, Analysis and Applications Wei Pan - Stochastic bathymetry in TQG Long Li - Stochastic Ekman models |
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25th Sandbox Workshop | Friday, 27th October | https://www.imperial.ac.uk/events/163419/25th-stuod-sandbox-workshop/ | On Stochastic slice model |
24th Sandbox Workshop | Friday, 30th June | https://www.imperial.ac.uk/events/160872/24th-stuod-sandbox-workshop-2/ | On Data Assimilation theory and application |
23rd Sandbox Workshop | Friday, 26th May | https://www.imperial.ac.uk/events/160870/23rd-stuod-sandbox-workshop/ | On Numerics |
22nd Sandbox Workshop | Friday, 28th April | https://www.imperial.ac.uk/events/160866/22nd-stuod-sandbox-workshop-2/ | On Thermal QG/Rotating Shallow Water |
21st Sandbox Workshop | Friday, 31st March | https://www.imperial.ac.uk/events/157058/21st-stuod-sandbox-workshop/ | On Wave Current Interactions |
20th Sandbox Workshop | Friday, 24th February | https://www.imperial.ac.uk/events/157055/20th-stuod-sandbox-workshop-2/ |
On Machine Learning |
19th Sandbox Workshop | Friday, 27th January | https://www.imperial.ac.uk/events/156254/19th-stuod-sandbox-workshop/ | On Data Analysis/Assimilation aligned with SWOT |
2024
31st Sandbox Workshop | Friday, 12 July |
SPEAKER: Arnaud Doucet TITLE: From Denoising Diffusions for Schrodinger bridges – Generative Modeling & Inference ABSTRACT: In the first part of the talk, I will review Denoising diffusion models, a powerful class of generative models. These models provide state-of-the-art results, not only for unconditional simulation, but also when used to sample from complex posterior distributions. In the second part of the talk, I will show how these ideas can be extended to propose novel methods to compute transport maps between two high-dimensional probability distributions and, in particular, to solve the Schrodinger bridge problem, an entropy-regularized version of optimal transport. I will demonstrate these methods on a variety of problems including unpaired fluid flows downscaling. and nonlinear filtering. 1200-1245: Arnaud Doucet 1245-1315: Discussion 1315-1330: Break 1330-1345: Mini-talk 1 – Alexander Lobbe 1345-1400: Mini-talk 2- Simon Benaichouche 1400-1430: Discussion & close |
On Generative Models |
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30th Sandbox Workshop | Friday, 28 June |
SPEAKER: Baylor Fox-Kemper TITLE: Building a bridge on both sides of a chasm saves 40% of the time ABSTRACT: Our group has been working on deterministic parameterizations and closures that align with the STUOD groups' efforts. I will provide updates on two fronts: coordinate-independent formulations of the parameterizations and ocean primitive equations of motion, and the Particle-in-Cell-for-Efficient-Swell (PiClES) wave model. 1430-1515: Baylor Fox-Kemper 1515-1545: Discussion 1545-1600: Break 1600-1615: Mini-talk 1 - Valentin Resseguier 1615-1630: Mini-talk 2 - Oliver Street 1630-1700: Discussion & close |
On Physical Models |
29th Sandbox Workshop | Friday, 31st May |
Speaker: Alberto Carrassi, Dept of Physics and Astronomy “Augusto Righi”, University of Bologna, IT Title: Using machine learning, data assimilation and their combination to improve a new generation of Arctic sea-ice models With: L. Bertino (NERSC, NO), M. Bocquet (ENPC, FR), J. Brajard (NERSC, NO), Y. Chen (U Reading, UK), S. Driscoll (U Reading, UK), C. Durand (ENPC, FR), A. Farchi (ENPC, FR), T. Finn (ENPC, FR), C. Jones (U Reading, UK), I. Pasmans (U Reading, UK) and F. Porro (U Bologna, IT) Abstract: We present an overview of the research efforts and results obtained in the context of the international project SASIP aimed at understanding and prediction the Arctic changes. We have been working on developing novel data assimilation, machine learning and their combination adapted to a new generation of sea-ice models that treats the ice as a brittle solid instead of as a fluid. These models present unique physical challenges such as sharp gradients, anisotropy and multifractality. In this talk we will first present the application of an ensemble variational method to estimate the state and parameters of the sea-ice model based on synthetic, satellite-like, data, illustrating the power and limitation of the available measurements. Second, we will show how to adapt the data assimilation procedure to the use of discontinuous Galerkin model, a modification that makes possible to assimilate very dense data (such as satellite) as well as to develop a scale-aware localisation procedure. To incorporate multifractal, anisotropic, and stochastic-like processes in sea ice, we envision the combination of geophysical sea-ice models together with neural networks in a hybrid modelling setup. On the one hand, deep learning can surrogate computationally expensive sea-ice models, on the other hand, deep learning can parametrize subgrid-scale processes in sea-ice models and correct persisting model errors, improving the forecasts by up to 70 % across all model variables on an hourly timescale. Finally, we will show how to use neural networks to emulate and replace a physical parametrization of the sea-ice melt ponds that create on the ice surface, and that have a major role on the albedo and thus on the general energy balance. Overall, our results show the potential of data assimilation and machine learning to extract much information from available data to correct model prediction and the models themselves. SCHEDULE 12:00-12:45 Alberto Carrassi 12:45-13:15 Discussion 13:15-13:30 Break 13:30-13:45 Mini-talk 1 – Alexander Lobbe 13:45-14:00 Mini-talk 2 – Mael Jaouen 14:00-14:30 Discussion |
On Data Assimilation |
28th Sandbox Workshop | Friday, 23 February |
Speaker: Peter Korn Title: Remarks on Virtual Euler Flows and Numerical Ocean Models Abstract: In this talk I present a discretisation of the incompressible Euler equations on polygonal prismatic meshes and describe associated discrete equivalents of continuous conservation laws. Capitalizing on the fact that the Euler equations form the heart of the dynamical core of numerical ocean circulation models I discuss implications of the numerical approach for the computational design and structural understanding of ocean models. The talk closes with outlining future research directions. SCHEDULE 12:00-12:45 Peter Korn 12:45-13:15 Discussion 13:15-13:30 Break 13:30-13:45 Mini-talk 1 - James Woodfield 13:45-14:00 Mini-talk 2 - Wei Pan 14:00-14:30 Discussion & Close
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On Numerical Methods |
27th Sandbox Workshop | Friday, 26 January |
SCHEDULE 12:00-12:20 Solange Coadou Chaventon 12:20-12:40 Yan Barabinot 12:40-13:10 Etienne Memin 13:10-13:20 Break 13:20-14:00 SWOT data TBD (likely Fabrice Collard) |
On SWOT data |