With ISO 27001 certification, Multiscale is enterprise-ready —delivering interpretable, physics integrated AI for complex scientific and industrial systems Atlanta, GA – Jun 23, 2025 – Multiscale Technologies, the leader in […]
A groundbreaking approach to solving data privacy challenges in connected mobility through intent-based AI agent coordination, allowing stakeholders to collaborate without exposing sensitive data. Matt Foster – Sales Director –
Vasu Kalidindi and Surya Kalidindi are the childhood friends behind Multiscale Technologies, a platform that uses cutting-edge AI to sharply reduce the number of physical experiments needed for business research
Surya Kalidindi, professor at Georgia Institute of Technology and CTO of Multiscale Technologies, provider of Advanced AI for Engineering, Manufacturing and Process Improvement challenges, was awarded AIME Honorary Membership during
AIME Honorary Membership is one of the highest honors that the Institute can bestow on an individual. Established in 1872 and bestowed in appreciation of outstanding service to the Institute
By Dr Surya Kalidindi – CTO, Multiscale Technologies | Papers & Citations. Introduction Engineering and scientific computing have long relied on forward solvers to simulate physical processes. These solvers predict
Molecular Dynamics (MD) simulations are essential for accurately predicting the physical and chemical properties of large molecular systems across various pressure and temperature ensembles. However, the high computational costs associated
High mix, low volume processes such as additive manufacturing (AM) offer tremendous promise for increasing the customization in manufacturing but are hindered by the lack of efficient methods for identifying
This article introduces systematically the foundational concepts undergirding the recently formulated AI (artificial intelligence)-based materials knowledge system (AI-MKS) framework. More specifically, these concepts deal with features engineering the heterogeneous material
This prospective offers a new Bayesian framework that could guide the systematic application of the emerging toolsets of machine learning in the efforts to address two of the central bottlenecks
