Integrated Artificial Intelligence (AI) & Machine Learning - Deep Learning with CFD & FEA Simulation
Machine learning is a method of data analysis that automates analytical model building. It is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. With Artificial Intelligence (AI) applications in CAE, that is Mechanical Engineering and FEA and CFD Simulations as design tools, our CAE engineers evaluate the possible changes (and limits) coming from Machine learning, whether Deep Learning (DL), or Support vector machine (SVM) or even Genetic algorithms to specify definitive influence in some optimization problems and the solution of complex systems.
Artificial Intelligence (AI) Twins can predict the outcome of simulation studies and can be used in the product development lifecycle when performing the traditional simulation is too costly or takes too much time.
Our AI team at Enteknograte uses the advanced CFD and FEA software in combination with Artificial Intelligence (AI) and Machine Learning tools Twins with the goal to train AI to learn from simulations, to extend the knowledge over time, and increase the performance and efficiency in the modeling process.
Machine Learning Algorithms
Machine learning algorithms all aim to learn and improve their accuracy as they process more datasets. One way that we can classify the tasks that machine learning algorithms solve is by how much feedback they present to the system. In some scenarios, the computer is provided a significant amount of labelled training data is provided, which is called supervised learning.
In other cases, no labelled data is provided and this is known as unsupervised learning. Lastly, in semi-supervised learning, some labelled training data is provided, but most of the training data is unlabelled. Let’s review each type in more detail:
- Supervised Learning
- Semi-supervised Learning
- Unsupervised Learning
Artificial Intelligence (AI) and Machine Learning (ML) in CFD & FEA
Finite Element Method and CFD has become the top physics-based simulation technique and the number of elements involved in a FEM and CFD simulation have increased by a factor of ten every decade. As a result of the increased problem size, the computing resources needed for FEA and CFD simulation in for example structural integrity, computational fluid dynamics (CFD), electromagnetic analysis, and structural topology optimization has grown dramatically and represent a non-trivial cost element in the design process. Artificial Intelligence (AI) and machine learning (ML) has been advancing and inventing new methods that address the complexity of the same design problems in FEA and CFD.
Recent advances in deep learning and the implementation of these methods using specially designed platforms running on GPU-based clusters are allowing ML models to shortcut the simulation process by summarizing the results of simulations. In doing so, the ML model serves as a repository of the wisdom gained from multiple simulation runs. The clear benefit of using ML is the reduction of number of simulation runs during the design of a new, but similar, product.
With AI & ML, FEA and CFD simulation changes from being a tool in the design cycle to a tool of data generation. Transforming from a platform of managing data, to a platform in which the product design lives and functions.
FEA and CFD simulation allow the modeling of the most complex systems, while ML can help optimize the use of simulation resources to make product designs more efficient without sacrificing accuracy.
Enteknograte team consist of talented engineers predominantly use a Python based stack for high-performance and low latency Machine Learning development with CFD and FEA based results training.
The people standing behind the Python ecosystem are truly amazing, and we wish them (and us) to continue their productive work to make the world better!
Applying AI and machine learning tools in the technological applications can enhance simulation efficiency, improve product quality and reduce production costs.
The combination of computational fluid dynamics (CFD) with machine learning (ML) is a recently emerging research direction with the potential to enable the solution of so far unsolved problems in many application domains. Machine learning is already applied to a number of problems in CFD, such as the identification and extraction of hidden features in large-scale flow computations, finding undetected correlations between dynamical features of the flow, and generating synthetic CFD datasets through high-fidelity simulations. These approaches are forming a paradigm shift to change the focus of CFD from time-consuming feature detection to in-depth examinations of such features, and enabling deeper insight into the physics involved in complex natural processes.
Deep learning is an ambiguous term used to denote a collection of models mainly implementing neural networks with many layers to challenging classification and estimation problems. The rapid growth of the power of deep learning techniques can be attributed to the development of parallelized versions of the deep learning models that can be run on GPU-based computer clusters.
This allows them to tackle problems of high complexity and simultaneously achieve high accuracy by being able to use large complex training datasets efficiently. This boosted the applicability of deep learning models to the difficult problem of representing the simulation results of FEM analysis used in product design on an industrial scale for real complex problems.
Deep Learning with PyTorch
PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab. PyTorch is a Python-based scientific computing package serving two broad purposes: A replacement for NumPy to use the power of GPUs and other accelerators. An automatic differentiation library that is useful to implement neural networks.
TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. Tensorflow is a symbolic math library based on dataflow and differentiable programming. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
ML & DL with Keras
Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. Up until version 2.3 Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. Keras is used by CERN, NASA, NIH, and many more scientific organizations around the world (and yes, Keras is used at the LHC). Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles.
Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.