22nd ECMI Conference Industry Day
Thursday at ECMI Conference is special, it is the Industry Day, during which company representatives will talk about how they apply modern mathematical tools in their business. from We will have speakers from:
- KGHM – Polish company, a major copper and silver producer in the world and also the main sponsor of the conference
- BNY Mellon – American investment banking company, one of the oldest banks in the world
- Satander Consumer Bank – part of Spanish multinational financial services company
- Nokia – Finnish multinational telecommunications, information technology, and consumer electronics corporation
- Sun Cable – Australian company that develops renewable energy networks in Oceania and Southeast Asia
- Saule Technologies – Polish company that develops next generation solar panels based on perovskite technology
- SatRev – Polish high-tech company that specialises in building small, lightweight nanosatellites
Additionally, at the end of this day we plan to organise an expert panel discussion on the future of industrial and applied mathematics.
Before the meetings with company representatives, there will be one Plenary lecture.
Engineer and applied mathematician at Central Research and Advance Engineering of Robert Bosch GmbH. Chief expert on methods for solving differential equations using AI methods. He will give a talk Taylor Mapping and Polynomial Neural Networks for solving forward and inverse Ordinary Differential Equations.
A second Plenary lecture discussing the mathematical methods of the digital twins. Using these virtual simulation models often requires solving ordinary differential equations and difficult inverse problems. They often contain parameters that are only partly unknown, or conversely have only small amount of data available. Both cases require innovative methods. The talk will discuss the Taylor mapping, in which the solution of the ODE with respect to the initial values is developed into a polynomial series. The associated Taylor weights are computed using ODEs and are valid for each initial value problem of this ODE. This algorithm can be interpreted as a Polynomial Neural Network where the neurons are polynomials, and the layers present a certain polynomial order.