Researchers will set up a start-up ‘AISoft,’ nearly million-fold faster than its peers in the field, to develop solutions to engineering problems in varied fields such as semiconductors, automobile.
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Indian Institute of Technology Madras researchers have developed algorithms that enable novel applications for Artificial Intelligence, Machine Learning and Deep Learning to solve engineering problems. The Researchers are going to establish a startup to deploy their Software called ‘AISoft’ to develop solutions to engineering problems in varied fields such as in thermal management, semiconductors, automobile, aerospace and electronic cooling applications.
, Machine Learning and Deep Learning are now being used for over a decade but traditionally only in areas such as signal processing, recognition, image reconstruction and prediction. Very limited attempts have been made globally in using these algorithms in solving engineering problems such as thermal management, electronic cooling industries, automobile problems like fluid dynamics prediction over a bonnet or inside the engine, aerospace industries like aerodynamics and fluid dynamics problems across an aero-foil or turbine engine.
A team of researchers lead by Dr Vishal Nandigana, Assistant Professor, Fluid Systems Laboratory, Department of Mechanical Engineering, IIT Madras, has developed and Deep Learning algorithms to solve engineering problems, which they do not solve a physical law to arrive at the solution of the system.
This idea is new and is only has now being looked by a few research groups across the world. Most of these research groups use Convolutional Neural Networks (CNN) or C-GAN (conditional generative adversarial network) to solve engineering problems.
Highlighting the unique aspects of these algorithms, Dr Vishal Nandigana said, “We tested AIsoft and used it to solve such thermal management problems. We found it to be nearly million-fold faster compared to existing solutions currently used in the field. Our works on any generalized rectilinear and curvilinear input geometry. Our research saves the computational time, which is the bottleneck to solve most engineering problems.”
The Researchers utilised a data-driven and a Deep Learning model to arrive at solutions for engineering problems after training the with data sets. These prior data sets can be from existing big data in the relevant engineering industry where there are lots of experimental data available. Also, if data is not available for training the , it can be generated using commercially-available CFD (Computational Fluid Dynamics) software on small independent pieces of the full-blown problem.
Further, Dr Vishal Nandigana said, “We are using a novel Recurrent Neural Network (RNN) and Deep Neural Network (DNN) to solve engineering problems. Our is grid or mesh independent and does not need information of left and right grid points to solve the grid point of interest. Our can work with sparse data sets and solve engineering problems. Our stands out in this aspect from commercially available software.”
IIT Madras Researchers have also developed hardware products using GPU and multi-threading processing to solve thermal management problems in thermal and electronic cooling industries. Both the software and hardware products are several times faster than commercial numerical method software and open source software in the market. These algorithms will solve a lot of pressing problems for industries and can also be used for educational purposes. […]