My research focuses on the development of efficient and parallelizable Monte Carlo (MC) methods for solving inverse problems. We design new machine learning techniques that can be used for characterizing geological reservoirs and evaluating parameter uncertainty. The inverse method is able to handle highly complex and realistic models from a Multiple-point Statistics (MPS) tool. A general framework is implemented in python and allows the algorithm to be embedded within many different applications.

Education

• MSc Degree in Applied Mathematics. École Polythechnique Fédérale de Lausanne, Switzerland, 2014.
• BSc in Mathematics.  École Polythechnique Fédérale de Lausanne, Switzerland, 2012.

Positions held

• 2015-now - PhD candidate, Université de Neuchâtel, Switzerland

Publications

• C. Jäggli, J. Straubhaar, and P. Renard, Posterior population expansion for solving inverse problems. Water Resour. Res.. (2017)
• L. Dedè, C. Jäggli, A. Quarteroni. Isogeometric numerical dispersion analysis for two-dimensional elastic wave propagation. Computer Methods in Applied Mechanics and Engineering, 284 (2015), 320-348
• C. Jäggli, L. Iapichino, G. Rozza. An improvement on geometrical parameterizations by transfinite maps. Comptes Rendus Mathématiques. 2014.