Fellowship - Super Accelerating Scientific Simulation for Physics-Based Real-Time Digital-Twin

Job Reference: 000326

Location: Japan

Closing Date: 21/04/2023

Job Posted Date: 28/11/2022

Salary: 10M JPY

Employment Type: Toshiba Fellowship

Business Type: Research & Development

Research Topic Number 2 - Super accelerating scientific simulation for physics-based real-time digital-twin 

Real-time digital-twin concepts as an inter-mediator taking care of the interaction among human, nature, robots and infrastructure systems would be to simulate the complex real world interactions as well as to run the real world in the cyber physical systems [1][2][3][4]. 
Digital-twin computing could well improve infrastructure resilience for various risks, and to control and optimize design, operation and maintenance in complex network systems.
Scientific simulations such as fluid mechanics, non-linear mechanics, multi-physics and multi-scale phenomena are implementations of theoretical models, they may run very slowly, even on modern computer hardware; for example, global climate models may take thousands of CPU-hours.
It is necessary to highly accelerate simulations --computer implementations of mathematical models—to test out new concepts and ideas, and explore potential behaviors of infrastructure systems and natural systems. 
 Toshiba has owned the data, model and knowhow of infrastructure systems, and has potential and environment to develop digital-twin computing technologies [1][2][3][4].

Expected Research Proposals
•    Physics-based real-time digital-twin for model predictive control by accelerating scientific simulation with machine learning method such as physics-informed neural networks, Hamiltonian networks, Galerkin projection and sparse identification, for computational fluid dynamics, continuum mechanics and nonlinear dynamics [5][6][7]. 
•    Accelerating scientific simulation utilizing quantum inspired technologies [8].
•    Making accurate prediction and control in complex network systems based high-dimensional data predictable method and control method [9][10][11][12].
•    Accelerating hybrid simulations of multi-agent simulation and physical simulation for a large number of risk scenarios in infrastructure systems [13].

[1] https://www.global.toshiba/ww/cps/corporate.html
[2] https://www.toshiba-energy.com/en/transmission/product/iot.htm
[3] https://asmedigitalcollection.asme.org/IMECE/proceedings-abstract/IMECE2021/85673/V011T11A003/1133129
[4] https://asmedigitalcollection.asme.org/IMECE/proceedings-abstract/IMECE2021/85697/V013T14A028/1133295
[5] https://arxiv.org/abs/2003.04630
[6] https://arxiv.org/abs/1906.01563
[7] https://arxiv.org/pdf/2001.08055.pdf
[8] https://arxiv.org/pdf/2106.05782.pdf
[9] https://www.pnas.org/content/115/43/E9994
[10] https://arxiv.org/abs/1701.03424
[11] https://stanfordasl.github.io/wp-content/papercite-data/pdf/McClellan.Lorenzetti.ea.PTRSA21.pdf
[12] https://www.nature.com/articles/s41467-021-21554-0.pdf
[13] https://arxiv.org/abs/2004.05185


This position is now closed. We are no longer accepting applications for this position.