Scientific Computing
Below are the InFoMM Mini-projects that fall within the mathematical area of scientific computing. To read more about each project please click on the title, this will open the project's lay report:
- Active wave absorption for polychromatic waves (USACE)
- Application of Deep Learning Techniques to Commercial Real Estate Appraisal (NewRock)
- Bayesian optimisation algorithms (NAG)
- Blade path optimisation in steam turbine design (Siemens)
- Blind source separation (Leonardo)
- Block preconditioning for incompressible two-phase flow (USACE)
- Choosing a fast initial propagator for rapid convergence of the Parareal Algorithm in the context of simple model problems (Culham Centre for Fusion Energy)
- Compressed sensing and matrix completion algorithms for demosaicing of spectral imagery (DSTL)
- Deflating magnetic oscillations (Culham Centre for Fusion Energy)
- Derivative-Free Optimization for Data Fitting (NAG)
- Development of multi-GPU algorithms for HPC (NVIDIA)
- Dynamic sectorisation over the West End region of UK airspace (NATS)
- Effect of transient disturbances in glass shaping processes (Schott AG)
- FE solvers for pressure and temperature equations (PDS)
- Herding Top Percentiles (Vodafone)
- Improved Algebraic Decoupling of Pressure Equation in Reservoir Simulation (Schlumberger)
- Improvement of nonlinear solver for reservoir simulation (Schlumberger)
- Mathematical formulation of the coarse predictor for the parareal algorithm in fusion plasma divertor simulations (Culham Centre for Fusion Energy)
- MLMC for 1-D edge plasma fluid model (Culham Centre for Fusion Energy)
- Optimization of the size of the time chunks used in implementing the Parareal Algorithm (Culham Centre for Fusion Energy)
- Robust vehicle routing (Tesco)
- Shape optimisation in Stokes flow (London Computational Solutions)
- Sketching for linear data fitting problems (NAG)
- Techniques for initialising simple data assimilation calculations for plasma models (Culham Centre for Fusion Energy)
- Uncertainty quantification through multilevel Monte Carlo simulation in FEniCS (Simula Research Laboratory)
- Weighted matrix completion for bioactivity prediction (e-Therapeutics)
- Well modelling constrained by real-time data (PDS)
- Wind Tunnel measurements of the aerodynamic forces on an F1 car: Optimisation of the path through car-state space (Williams F1)
- Optimisation methods for machine learning applications (NAG)