New Theoretical and Numerical Methods for Wave-Motion Modeling and Optimization

Abstract

Wave-motion modeling and mathematical optimization have a vast variety of applications in research and industry. Marcus Noack, PhD fellow at Simula Research Laboratory, delved deep into the theoretical and numerical principles that underly these concepts. Through the intensive investigation of these numerical principles, Noack developed three revolutionary theoretical and numerical methods that can be utilized to improve optimization and wave modeling techniques in the energy and technology sector. For example, the Hybrid Genetic Deflated Newton (HGDN) method he developed provides risk assessment for industrial optimization problems such as, wind farm optimization and seismic imaging. Improving risk assessment in these industries leads to cost reduction and allows more resources to be spent on increasing energy needs. Noack’s other developed methods that combine wave-front propagation and wavemotion modeling can be used in collaboration with the HGDN algorithm to yield more efficient wave imaging. For example, an energy company images the subsurface of the Earth with seismic imaging to find natural resources or a doctor images a patient’s organs to make a diagnosis.