AI and Machine Learning


We offer science-based Al solutions for engineering applications with focus on fluid mechanics and soft material design and discovery, for a wide range of industries from consumer products, to chemical and pharmaceuticals, and to oil and gas.


Our venture, RhIMLIES, is centered around providing engineering solutions to complex soft materials and fluid problems across different industries and disciplines based on state-of-the-art science-based machine learning methodologies. Many engineering/scientific software packages are used every day to perform fluid mechanical and rheological simulation of a given geometry/material/processing condition; However, none of these find the same success in the industrial setting as they do in academic environments, due to lack of accuracy, adaptability, and ease of use. There is a clear need for data-driven, smart, agile and adjustable solutions that rely on limited number of data to provide scientific and reliable predictions to processing and material properties in wide range of industries.

We have developed a methodology built upon physics-informed neural networks to benefit from the vast application of machine learning algorithms. Our methodology leverages the most recent advances in the world of science-based machine learning to accelerate material design and discovery as well as process control and design for complex and multi-component fluid systems.

We have demonstrated that product design, from formulation to properties and testing can be as short as 2 months for a complex consumer product using this method, as opposed to 2 years in traditional R&D process. A huge leap forward is the ease of use and adjustability of the method for non-expert users across different industries, as opposed to well-trained engineers and scientists needed to use the traditional software and solutions.


Safa Jamali, PhD is a faculty member in mechanical and industrial engineering department in the College of Engineering.  will lead the project administration, supervision, conceptualization, and execution of the project. 

Mohammadamin Mahmooudabadbozchelou is a PhD candidate in mechanical and industrial engineering department in the College of Engineering, and will lead the effort on product development, case studies, validation, programming of the project. 

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