Fujitsu Laboratories of Europe has announced the development of a new AI-based technology and platform, designed to convert traditional physics-based simulators into fast, highly accurate AI simulators.
Fujitsu’s AI Solver represents an important breakthrough for industrial applications, revolutionizing fields such as CAE, product design and autonomous smart device performance. In the case of CAE, simulation plays a key role in reducing the number of costly prototype and product failures, supporting design decisions as well as for verification and validation.
The AI Solver revolutionizes the speed of the simulation process, with commensurate business advantages. The platform is the result of a joint development program between Fujitsu Laboratories Ltd, Fujitsu Advanced Technologies Ltd and Fujitsu Laboratories of Europe.
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. Fujitsu has reduced this from hours to just milliseconds, without compromising performance (< 2% discrepancy compared to physics-based counterparts).
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 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. This involves learning from large databases of simulation results while data is still being generated, reducing the required time by one third.
The potential applications for Fujitsu’s AI Solver are extensive. For product design applications, it can enable designers to receive real-time feedback rather than waiting hours for results. 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.
Dr 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. 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.
“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. Additionally, this had to be achieved in a consistent and generic way, rather than on a case-by-case basis. 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.”
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.
“In addition, designers are forced to evaluate multiple design scenarios in tighter schedules, making physical prototyping for the evaluation of design alternatives both time-consuming and costly.
“To address these challenges, designers can now use AI simulators, allowing them to test a design virtually in order to gauge performance for numerous scenarios in a very short timespan. As a result of Fujitsu’s breakthrough technology, the whole process is dramatically optimised.”
Examples of Fujitsu’s AI Solver platform include the conversion of two very different types of physical simulators into AI simulators. The first involves a 3D heat transfer simulator that models the thermal interaction between solids and fluids, often used to design and verify the cooling of electronics. It consists of a multi-physics simulation that requires the handling of multiple material properties, power sources and radiation.
The second example is a computational electromagnetic simulator that models the magnetization of a solid that is subjected to the influence of an external magnetic field, often used for the design of hard drive heads or other memory devices. As shown in the examples below, the results of the reference physics-based simulators and their AI counterparts are almost indistinguisble, in that the discrepancy is less than 2%.