Morgan Kerhouant

Quaisr, UK



Morgan carried out his PhD in the department of chemical engineering at Imperial College London. During this time he worked on numerical modelling, computational fluid dynamics and machine learning for a range of problems in the energy industry. As part of his PhD he developed a hybrid multi-physics solver for alkaline water electrolysers, accelerated through physics-informed neural networks. He also has experience with uncertainty quantification, sensitivity analysis and optimisation. He is now working as a software engineer for Quaisr, focusing on applications related to machine learning and simulations. The company is venture capital backed, works across a range of verticals and helps in enterprise wide deployment and scale up of R&D models into production.


Enterprise deployment, scaling and democratisation of R&D models


We highlight the challenges in getting a modelling workflow running on a user's laptop to deploying it at scale and as a service on the cloud. We talk about the democratisation aspects and making these tooling accessible to all internal stakeholders within an organisation. We talk about the issues from the perspective of multiple stakeholders, connectivity and collaboration pains and the value of streamlining the process at a platform level to accelerate R&D cycles and decision making. To put things into perspective we use an example of interfacing grammatical evolution with a computational chemistry simulator for materials discovery.
Scroll to Top