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.
Computational Fluid Dynamics wind turbine ODYSSEE AI & Machine Learning for CFD, FEA, Statistics, Data Mining, Data Fusion, Optimization and Robustnes

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.

Artificial Intelligence (AI) Machine learning ML python PyTorch, Tensorflow, NumPy, SciPy, Cython, Flask, Django, Matplotlib, Pandas, Keras, Jupyter, FastAPI, SQLAlchemy 3

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.

Artificial Intelligence (AI) Machine learning ML python PyTorch, Tensorflow, NumPy, SciPy, Cython, Flask, Django, Matplotlib, Pandas, Keras, Jupyter, FastAPI, SQLAlchemy 3
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.

Artificial Intelligence (AI) Machine learning ML python PyTorch, Tensorflow, NumPy, SciPy, Cython, Flask, Django, Matplotlib, Pandas, Keras, Jupyter, FastAPI, SQLAlchemy 3
Structures drone ODYSSEE AI & Machine Learning for CFD, FEA, Statistics, Data Mining, Data Fusion, Optimization and Robustness

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!

System-Dynamics ODYSSEE AI & Machine Learning for CFD, FEA, Statistics, Data Mining, Data Fusion, Optimization and Robustnes

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.

Artificial Intelligence (AI) Machine learning ML python PyTorch, Tensorflow, NumPy, SciPy, Cython, Flask, Django, Matplotlib, Pandas, Keras, Jupyter, FastAPI, SQLAlchemy 3
PyTorch CFD Finite element method fea Deep Learning ML Artificial Intelligence (AI) pythonTensorflow, NumPy, SciPy, Cython, Flask, Django, Matplotlib, Pandas, Keras, Jupyter, FastAPI, SQLAlchemy

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 CFD Finite element method fea Deep Learning ML Artificial Intelligence AI python Pytorch NumPy SciPy Cython Flask Django Matplotlib Pandas Keras Jupyter FastAPI SQLAlchemy


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.

Keras CFD Finite element method fea Deep Learning ML Artificial Intelligence (AI) python TensorFlow Pytorch NumPy SciPy Cython Flask Django Matplotlib Pandas Jupyter FastAPI SQLAlchemy

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 CFD Finite element method fea Deep Learning ML Artificial Intelligence (AI) python TensorFlow Pytorch keras NumPy SciPy Cython Flask Django Matplotlib Pandas Jupyter FastAPI SQLAlchemy 2


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.


We pride ourselves on empowering each client to overcome the challenges of their most demanding projects.

Enteknograte offers a Virtual Engineering approach with FEA tools such as MSC Softwrae(Simufact, Digimat, Nastran, MSC APEX, Actran Acoustic solver), ABAQUS, Ansys, and LS-Dyna, encompassing the accurate prediction of in-service loads, the performance evaluation, and the integrity assessment including the influence of manufacturing the components.

Special Purpose AI Software and Customized GUI Development Based on Your Industry and Your Requirements: Finite Element and CFD Softwares Integration with AI and Deep Learning Platform.

With the integration of AI technology with physics-based simulations, we can take a significant step forward to the most optimized design in a very short time.  In the proposed method, the loss functions and neural network weights are updated directly using gradient information from the physics model obtained from finite element and CFD analysis.

The key idea is that these gradients are calculated automatically through the data from finite element and CFD solver and then backpropagated to the deep learning neural network during the training or intelligence building process. This integrated optimization approach will be implemented in Python and C++ programming languages. The information exchange between commercial software such as Ansys Fluent, Siemens Star-ccm+, Abaqus, Comsol, LS-Dyna and Nastran as CFD and FEA solvers, and Deep learning platform (A.I process) will be done automatically with a special interface developed for them. You will have a very user-friendly and simple interface that is specialized and customized for your problem and developed based on your preferred FEA and CFD simulation software. We cover almost all main FEA and CFD software that clients prefer in their industry and business area.


Crash Test and Crashworthiness

Finite Element Simulation of Crash Test and Crashworthiness with LS-Dyna, Abaqus and PAM-CRASH Including Airbag & Seat Belt Effectiveness, Trucks, Bus and eVTOL.
Enteknograte engineers simulate the crash safety with innovative CAE and virtual prototyping available in the non-linear structural codes: LS-DYNA, PAM-CRASH, RADIOSS and ABAQUS. We offer advanced FEA modeling consultancy services. We are experienced with automotive crash safety and consumer crash test protocols such as (frontal impact), (side impact), IIHS and EuroNCAP. Our engineers have Tier1 backgrounds in FEM (Finite Element Method) and are fluent in the codes: LS-DYNA, PAM-CRASH, RADIOSS and ABAQUS.

Blast Resistance with Protection Against Ballistic Attacks

FEA (Finite Element Analysis) & CFD Based Simulation of Blast, Explosion & Fire
Enteknograte engineers simulate the Blast and Explosion with innovative CAE and virtual prototyping available in the non-linear structural codes MSC Dytran, LS-DYNA, Ansys Autodyn, and ABAQUS. Enteknograte Engineers can simulate any type of Blast and Explosion such as air blast, Underwater explosion (UNDEX) and Fragmentation due to blast to survey structural integrity in High Rate Loading Condition.

Seat Design: Finite Element and CFD Simulation for Static & Dynamic Comfort, Whiplash, Thermal Comfort, Crash Test

Simulation Based Design can help us to ensure the right occupant posture, which is essential for safety, Static and Dynamic Comfort, for example by predicting the H-Point and incorporating whiplash, thermal and Acoustic comfort simulation. The ability to predict the comfort of innovative seat designs using simulation tools, a library of human models with our team experience in CFD (Siemens Start-ccm+, Ansys Fluent and OpenFoam) and FEA (Ansys LS-DYNA, Simulia Abaqus, ESI Pam-Crash and Altair RADIOSS) simulation software with integrated Artificial Intelligence and Machine Learning for innovative design, can help manufacturers to create seats that provide a superior driving experience for their customers.

eVTOL (Electric Vertical Take-Off and Landing) & UAM (Urban Air Mobility)

FEA & CFD Based Simulation for Airworthiness Certification, Aerodynamics, Aeroacoustics and Crashworthiness
The VTOL, eVTOL and UAM market is constantly changing and evolving, so maintaining a competitive edge both within the industry and supporting mission effectiveness requires significant research and development activities. Enteknograte offers the industry’s most complete simulation solution for Urban Air Mobility (UAM) and Vertical Take off and Landing (VTOL) aircrafts.

Finite Element Welding Simulation: RSW, FSW, Arc, Electron and Laser Beam Welding

Enteknograte engineers simulate the Welding with innovative CAE and virtual prototyping available in the non-linear structural codes such as LS-DYNA, Ansys, Comsol, Simufact Welding, ESI SysWeld and ABAQUS. The Finite element analysis of welding include Arc Welding, laser Beam Welding, RSW, FSW and transfer the results of welding simulation for next simulation like NVH, Crash test, Tension, Compression and shear test and fatigue simulation. We can develop special purpose user subroutine (UMAT) based on clients need to empower simulation environment to overcome any complicated problem in heat load condition, phase change and user defined material constitutive equation.

Metal Forming Simulation: FEA Based Design and Optimization

Using advanced Metal Forming Simulation methodology and FEA tools such as Ansys, Simufact Forming, Autoform, FTI Forming, Ls-dyna and Abaqus for any bulk material forming deformation, combining with experience and development have made Enteknograte the most reliable consultant partner for large material deformation simulation: Closed die forging Open die forging processes such as cogging, saddling, and other GFM, processes Rolling for long products, Extrusion, Ring Rolling, Cross Wedge Rolling and Reducer Rolling for pre-forming Cold forming, Sheet metal forming.

Casting: Finite Element and CFD Simulation Based Design

Using Sophisticated FEA and CFD technologies, Enteknograte Engineers can predict deformations and residual stresses and can also address more specific processes like investment casting, semi-solid modeling, core blowing, centrifugal casting, Gravity Casting (Sand / Permanent Mold / Tilt Pouring), Low Pressure Die Casting (LPDC), High Pressure Die Casting (HPDC), Centrifugal Casting and the continuous casting process. The metal casting simulation using FEA and CFD based technologies, enable us to address residual stresses, part distortion, microstructure, mechanical properties and defect detection.

Additive Manufacturing and 3D Printing

FEA Based Design and Optimization with Simufact, Abaqus, ANSYS and MSC Apex for powder bed fusion (PBF), directed energy deposition (DED) and binder jetting processes
With additive manufacturing, the design is not constrained by traditional manufacturing requirements and specific number of design parameters. Nonparametric optimization with new technologies such as Artificial Intelligence in coupled with Finite Element method, can be used to produce functional designs with the least amount of material. Additive manufacturing simulations are key in assessing a finished part’s quality. Here at Eneteknograte, dependent of the problem detail, we use advanced tools such as MSC Apex Generative Design, Simufact Additive, Digimat, Abaqus and Ansys.

Finite Element Analysis of Durability and Fatigue Life

Vibration Fatigue, Creep, Welded Structures Fatigue, Elastomer and Composite Fatigue with Ansys Ncode, Simulia FE-Safe, MSC CAEFatigue, FEMFAT
Durability often dominates development agendas, and empirical evaluation is by its nature time-consuming and costly. Simulation provides a strategic approach to managing risk and cost by enabling design concepts or design changes to be studied before investment in physical evaluation. The industry-leading fatigue Simulation technology such as Simulia FE-SAFE, Ansys Ncode Design Life and FEMFAT used to calculate fatigue life of multiaxial, welds, short-fibre composite, vibration, crack growth, thermo-mechanical fatigue.

Heat Transfer and Thermal Analysis: Fluid-Structure Interaction with Coupled CFD and Finite Element Based Simulation

We analyze system-level thermal management of vehicle component, including underhood, underbody and brake systems, and design for heat shields, electronics cooling, HVAC, hybrid systems and human thermal comfort. Our Finite Element (LS-Dyna, Ansys, Abaqus) and CFD simulation (Siemens Start-ccm+, Ansys Fluent , Ansys CFX and OpenFoam) for heat transfer analysis, thermal management, and virtual test process can save time and money in the design and development process, while also improving the thermal comfort and overall quality of the final product.

Hydrodynamics: Coupled CFD and FEA simulation for FSI Analysis

Marine and offshore structures, Transient Resistance, Propulsion, Sea-Keeping and Maneuvering Simulation, Cavitation, Acoustics, Vibration and Fatigue
Hydrodynamics is a common application of CFD and a main core of Enteknograte expertise for ship, boat, yacht, marine and offshore structures simulation based design. Coupling Hydrodynamic CFD Simulation in Ansys Fluent, Siemens Star-ccm+ and MSC Cradle with structural finite element solver such as Abaqus and Ansys, enable us to Simulate most complicated industrial problem such as Cavitation, Vibration and Fatigue induced by hydrodynamics fluctuation, Transient Resistance, Propulsion, Sea-Keeping and Maneuvering Simulation, considering two way FSI (Fluid Structure Interaction) coupling technology.

Aerodynamics Simulation: Coupling CFD with MBD, FEA and 1D-System Simulation

Aerodynamics studies can cover the full speed range of low speed, transonic, supersonic and hypersonic flows as well as turbulence and flow control. System properties such as mass flow rates and pressure drops and fluid dynamic forces such as lift, drag and pitching moment can be readily calculated in addition to the wake effects. This data can be used directly for design purposes or as in input to a detailed stress analysis. Aerodynamics CFD simulation with sophisticated tools such as MSC Cradle, Ansys Fluent and Siemens Star-ccm+ allows the steady-state and transient aerodynamics of heating ventilation & air conditioning (HVAC) systems, vehicles, aircraft, structures, wings and rotors to be computed with extremely high levels of accuracy.

Acoustics and Vibration: FEA and CFD for AeroAcoustics, VibroAcoustics and NVH Analysis

Noise and vibration analysis is becoming increasingly important in virtually every industry. The need to reduce noise and vibration can arise because of government legislation, new lightweight constructions, use of lower cost materials, fatigue failure or increased competitive pressure. With deep knowledge in FEA, CFD and Acoustic simulation, advanced Acoustic solvers and numerical methods used by Enteknograte engineers to solve acoustics, vibro-acoustics, and aero-acoustics problems in automotive manufacturers and suppliers, aerospace companies, shipbuilding industries and consumer product manufacturers.

FEA Based Composite Material Design and Optimization: MSC Marc, Abaqus, Ansys, Digimat and LS-DYNA

Finite Element Method is an efficient tool for development and simulation of Composite material models of Polymer Matrix Composites, Metal Matrix Composites, Ceramic Matrix Composites, Nanocomposite, Rubber and Elastomer Composites, woven Composite, honeycomb cores, reinforced concrete, soil, bones ,Discontinuous Fiber, UD Composit and various other heterogeneous materials. Enteknograte Engineers are very skilled in design of composite structural parts for crash and impact analysis using advanced finite element tools: Deformation and damage analysis, Material failure predictions, Drop and crushing testing, High-speed and hypervelocity impacts, Highly nonlinear transient dynamic forces, Explosive loading and forming.

CFD and FEA in Civil Engineering: Seismic Design, Earthquake, Tunnel, Dam, Concrete Structures and Geotechnical Multiphysics Simulation

Enteknograte, offer a wide range of consulting services based on many years of experience using FEA and CFD: Coupled/Multiphysics problems: mechanics of porous media, spalling of concrete, freezing of ground and young hardening concrete, Borehole stability problems, Constitutive modeling of concrete, Settlement damage on concrete and masonry constructions, Pipelines, Earthquake analysis, Tunnel, Dam and Geotechnical Multiphysics Simulation.

Automotive Engineering

Crash Test, Seat Design, Powertrain Component Development, NVH, Combustion, Thermal Simulation, Welding, Casting & Forming Technologies
We focus on strategic use of CAE to Optimise Designs, investigate and resolve problems, and minimise time and cost to market. Advanced Crash Test Simulation, Seat Design, Powertrain Component Development, NVH, Combustion, Thermal Simulation, Welding, Casting and Forming Technologies, Geartrain modelling and analysis software packages, provides the foundation of concept and definitive design for all driveline and transmission projects that we undertake.

In Silico Medical & Biomedical Device Testing: Finite Element & CFD Simulation and Design, Considering FDA & ASME V&V 40

Enteknograte Biomedical Engineers use FEA and CFD for simulating: Orthopedic products, Medical fasteners, Ocular modeling, Soft tissue simulation, Packaging, Electronic systems, Virtual biomechanics, Knee replacement, Human modeling, Soft tissue and joint modeling, Hospital equipment, Laser bonding, Ablation catheters, Dental implants, Mechanical connectors, Prosthetics, Pacemakers, Vascular implants, Defibrillators, Heart valve replacements.

Electromagnetic Multiphysics FEA & CFD Based Simulation

Enteknograte Finite Element Electromagnetic Field simulation solution which uses the highly accurate finite element solvers and methods such as Ansys Maxwell, Simulia Opera, Simulia CST, JMAG, Cedrat FLUX, Siemens MAGNET and COMSOL to solve static, frequency-domain, and time-varying electromagnetic and electric fields includes a wide range of solution types for a complete design flow for your electromagnetic and electromechanical devices in different industries.

Robots Dynamics & Performance Assessment: Coupled MBD & FEA Simulation-Based Design

Robot designers can increase the performance of their products by using Coupled FEA and MBD software such as Ansys, Abaqus, Simpack and MSC Adams multibody simulation (MBS) software to simulate the transient dynamic behavior of the complete robot mechanism and control algorithm.

Full Vehicle MultiBody Dynamics Simulation: Car Ride, Driveline, Engine and Tire MBD

With MultiBody Dynamic Simulation, you can perform various analyses on the vehicle to test the design of the different subsystems and see how they influence the overall vehicle dynamics. This includes both on- and off-road vehicles such as cars, trucks, motorcycles, buses, and land machinery. Typical full vehicle analysis includes handling, ride, driveline, comfort, and NVH. Automotive models are also used for Realtime applications (HiL, SiL, and MiL). We can also examine the influence of component modifications, including changes in spring rates, damper rates, bushing rates, and anti-roll bar rates, on the vehicle dynamics.

CFD Simulation of Reacting Flows and Combustion

Gas Turbine, Fuel Injector & Spray, Exhaust Aftertreatment with Detailed Chemistry
By using Accurate reaction mechanisms that representing every class of reaction important for combustion analysis and combination of advanced computational fluid dynamics (CFD) combustion simulation tools such as Kiva, Ansys Fluent, Ansys Forte, AVL Fire, Converge CFD, Siemens Star-ccm+ , MSC Cradle and System Modeling software such as Matlab Simulink and GT-Suite enable Enteknograte engineering team to reduce chemistry analysis time by orders of magnitude, virtually eliminating the bottleneck that chemistry integration produces during the simulation process.

NVH & Acoustics for Hybrid & Electric Vehicles

In NVH Engineering and simulation of Hybrid/Electric Vehicles, the noise from tire, wind or auxiliaries, which consequently become increasingly audible due to the removal of the broadband engine masking sound, should be studied. New noise sources like tonal sounds emerge from the electro-mechanical drive systems and often have, despite their low overall noise levels, a high annoyance rating. Engine/exhaust sounds are often used to contribute to the “character” of the vehicle leads to an open question how to realize an appealing brand sound with EV.