Multiscale Technologies

The core of Multiscale's offering is the MIND Platform, a no-code, physics-informed AI system designed for engineering environments. This platform integrates data from physical experiments and simulations into a unified, continuously learning workflow. It is engineered to handle sparse, noisy, and high-dimensional data, which is common in industrial R&D. A key feature is its "inverse solver," which works backward from a desired performance outcome to identify the necessary input parameters. The platform combines domain-aware machine learning, Bayesian optimization, and active learning to accelerate development cycles, reduce the number of physical experiments, and optimize processes. It also includes a Data Catalog for organizing fragmented data sources and an App Hub for building custom engineering applications.

Multiscale Technologies serves a diverse client base across several industrial and manufacturing sectors, including aerospace, automotive and electric vehicles, semiconductors, metals, chemicals, and energy. The business model centers on providing this platform to R&D and manufacturing teams to accelerate innovation and solve complex physical problems. For instance, clients in the semiconductor industry use the platform for process optimization and yield prediction, while automotive clients leverage it for battery innovation and structural optimization. By creating high-fidelity digital twins and employing probabilistic modeling, the company enables clients to make more accurate decisions, reduce waste, and bring products to market faster. Keywords: physics-informed AI, materials informatics, manufacturing process optimization, digital twin, simulation acceleration, engineering software, materials discovery, R&D automation, industrial AI, Bayesian optimization, active learning, semiconductor manufacturing, automotive R&D, aerospace engineering, chemical process modeling, advanced materials, no-code AI platform, data-driven engineering, multiscale modeling, predictive maintenance, yield optimization, structural optimization, battery innovation, high-dimensional data analysis, materials science