STUOD Sandbox Workshops


4th  Sandbox Workshop Friday, 30th October On examining TGQ  
3rd  Sandbox Workshop Friday, 4th September On examining Exemplar 2  
2nd  Sandbox Workshop Friday, 3rd July On examining Exemplar 1  
1st  Sandbox Workshop Friday, 29th May    


12th Sandbox Workshop Friday, 3 December On the observation errors
11th Sandbox Workshop Friday, 5 November On practical parameterisation of the subscale 
10th Sandbox Workshop Friday, 5 November  
9th Sandbox Workshop Friday, 9 July On data coming from model experimental and numerical data
8th Sandbox Workshop Friday, 7 May On examining stochastic partial differential equations (SPDEs)
7th Sandbox Workshop Friday, 12 March On data coming from model experimental and numerical data
6th Sandbox Workshop Friday, 12 February On LU Models
5th Sandbox Workshop Friday, 15 January On Wave Current Interactions


18th Sandbox Workshop Friday, 25 November  On High Resolution Numerical Simulation Data
17th Sandbox Workshop Friday, 24 June  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 On the collaborative research with the University of Twente
13th Sandbox Workshop Friday, 28 January On Data Assimilation 


26th Sandbox Workshop Friday, 8th December


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

25th Sandbox Workshop Friday, 27th October On Stochastic slice model
24th Sandbox Workshop Friday, 30th June  On Data Assimilation theory and application
23rd Sandbox Workshop Friday, 26th May  On Numerics
22nd Sandbox Workshop Friday, 28th April  On Thermal QG/Rotating Shallow Water
21st Sandbox Workshop Friday, 31st March On Wave Current Interactions
20th Sandbox Workshop Friday, 24th February

On Machine Learning

19th Sandbox Workshop Friday, 27th January On Data Analysis/Assimilation aligned with SWOT




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
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.


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.  


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


On Numerical Methods
27th Sandbox Workshop Friday, 26 January


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