- Unit:
- SINTEF Digital
- Department:
- Mathematics and Cybernetics
- Office:
- Oslo
I've been working in the Mathematics and Cybernetics department since 2014, now as a research scientist in the Computational Geosciences research group. Between 2017 and 2020, I did my PhD on efficient mathematical methods for prediction of drift trajectories in the ocean, and prior to that I worked in various EU projects in the Heterogeneous Computing research group.
Education
PhD in Mathematical Sciences, NTNU, 2020. "Efficient Forecasting of Drift Trajectories using Simplified Ocean Models and Nonlinear Data Assimilation on GPUs"
Master's degree in Industrial Mathematics, NTNU, 2014. "A CUDA Back-End for the Equelle Compiler"
Competence and research areas
Even though it is now possible to run large and accurate simulations of many complex geophysical processes, we are often faced with practical limitations due to uncertain model parameters or initial states.
My expertise lies in the intersection between statistical mathematics, numerical simulation, and high-performance computing. I work on mathematical methods that incorporate noisy and partial observations into numerical simulations of geophysical systems using ensemble-based data assimilation.
Highlighted publications
- Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting
- Coastal ocean forecasting on the GPU using a two-dimensional finite-volume scheme
- Efficient Forecasting of Drift Trajectories using Simplified Ocean Models and Nonlinear Data Assimilation on GPUs
- Evaluation of selected finite-difference and finite-volume approaches to rotational shallow-water flow
- GPU Computing with Python: Performance, Energy Efficiency and Usability
Other publications
- Numerical Comparison Between ES-MDA and Gradient-Based Optimization for History Matching of Reduced Reservoir Models
- Ensemble Simulations With MRST With Examples from CO2 Foam Simulation
- Ensemble simulations in MRST using the ensemble module
- Ensemble simulations in MRST using the ensemble module
- Comparison of Ensemble-Based Data Assimilation Methods for Drift Trajectory Forecasting
- Ensemble-based Data Assimilation Methods for Drift Trajectory Forecasting
- Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs
- Performance and Energy Efficiency of CUDA and OpenCL for GPU Computing using Python
- Data Assimilation for Ocean Drift Trajectories using Massive Ensembles and GPUs
- Drift Trajectory Predictions using Massive Ensembles of Simplified Ocean Models