Application of QUBO solver using black-box optimization to structural design for resonance avoidance

Quadratic unconstrained binary optimization (QUBO) solvers can be applied to design an optimal structure to avoid resonance. QUBO algorithms that work on a classical or quantum device have succeeded in some industrial applications. However, their applications are still limited due to the difficulty of transforming from the original optimization problem to QUBO. Recently, black-box optimization (BBO) methods have been proposed to tackle this issue using a machine learning technique and a Bayesian treatment for combinatorial optimization. We employed the BBO methods to design a printed circuit board for resonance avoidance. This design problem is formulated to maximize natural frequency and simultaneously minimize the number of mounting points. The natural frequency, which is the bottleneck for the QUBO formulation, is approximated to a quadratic model in the BBO method. We demonstrated that BBO using a factorization machine shows good performance in both the calculation time and the success probability of finding the optimal solution. Our results can open up QUBO solvers' potential for other applications in structural designs.

Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics

Machine learning approaches have recently been leveraged as a substitute or an aid for physical/mathematical modeling approaches to dynamical systems. To develop an efficient machine learning method dedicated to modeling and prediction of multiscale dynamics, we propose a reservoir computing model with diverse timescales by using a recurrent network of heterogeneous leaky integrator neurons. In prediction tasks with fast-slow chaotic dynamical systems including a large gap in timescales of their subsystems dynamics, we demonstrate that the proposed model has a higher potential than the existing standard model and yields a performance comparable to the best one of the standard model even without an optimization of the leak rate parameter. Our analysis reveals that the timescales required for producing each component of target dynamics are appropriately and flexibly selected from the reservoir dynamics by model training.