We propose an innovative method for computational science for sustainable promotion of scientific discovery by supercomputers in the Exascale Era by combining (Simulation + Data + Learning (S+D+L)). In May 2021, we started operation of the Wisteria/BDEC-01 system with 33+PF at University of Tokyo. It is a Hierarchical, Hybrid, Heterogeneous (h3) system, which consists of computing nodes for CSE with A64FX and those for Data Analytics/AI with NVIDIA A100 GPU’s. We develop a software platform “h3-Open-BDEC” for integration of (S+D+L) and evaluate the effects of integration of (S+D+L) on the Wisteria system. The h3-Open-BDEC is designed for extracting the maximum performance of the supercomputers with minimum energy consumption focusing on (1) innovative method for numerical analysis with high-performance/high-reliability/power-saving based on the new principle of computing by adaptive precision, accuracy verification and automatic tuning, (2) Hierarchical Data Driven Approach (hDDA) based on machine learning, and (3) Software for heterogeneous systems, such as Wisteria/BDEC-01. Integration of (S+D+L) by h3-Open-BDEC enables significant reduction of computations and power consumption, compared to those by conventional simulations. In this presentation, the speaker will describe the activities related to Wisteria/BDEC-01 and h3-Open-BDEC. Furthermore, near future plan for supercomputer systems in the University of Tokyo is presented.