Mr Akihiko Miyazawa, CEO of FATEC elaborates from the design perspective: “Design for electronics devices is a complex, demanding task that necessitates balancing numerous competing objectives with component arrangement, stringent temperature constraints, size restrictions, weight limitations and widely varying operating conditions. While in the short term our technology targets traditional users of CAE such as designers, the potential applications go well beyond product design and include increasing the efficiency of smart devices, such as robots, when guided by real-time simulations rather than heuristics.” Additionally, this had to be achieved in a consistent and generic way, rather than on a case-by-case basis. A key challenge for us was being able to incorporate all the features of the original solver rather than only allowing users to modify the geometry. The conversion of traditional physics-based simulators into AI simulators is an important breakthrough, bringing the time taken for a single simulation down from hours to milliseconds, thus delivering real-time results.
#ANALYTIC SOLVER PLATFORM COST SOFTWARE#
For smart devices, involving robots that need to adapt to their environment autonomously, real-time simulation results would dramatically increase both efficiency and automony compared to the use of simple heuristics.ĭr Adel Rouz, CEO of Fujitsu Laboratories of Europe explains: “While the advent of HPC and cloud computing has transformed the simulation process by reducing the associated hardware and software costs, we have not yet seen this translated into a significant reduction in the time taken to perform individual simulations. For product design applications, it can enable designers to receive real-time feedback rather than waiting hours for results. The potential applications for Fujitsu’s AI Solver are extensive. This involves learning from large databases of simulation results while data is still being generated, reducing the required time by one third. Fujitsu has combined multiple elements to achieve the performance of the AI Solver, using AI-based simulators’ deep neural network data characteristics to reproduce the behavior of physics-based simulators and automatically create highly targeted approximations in real-time compared to hours or days.
The generation of large databases of simulation results, together with the training of large deep neural networks, are complex and time-consuming tasks.
Fujitsu has reduced this from hours to just milliseconds, without compromising performance (< 2% discrepancy compared to physics-based counterparts).
Fujitsu’s AI Solver significantly accelerates the time-to-solution for physics-based simulations, which typically involve complicated calculations that can take several hours for just one process.
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