Computational Engineering is an emerging discipline that deals with the development and application of computational models for engineering, known as Computational Engineering Models[1] or CEM. Computational engineering uses computers to solve engineering design problems important to a variety of industries.[2] At this time, various different approaches are summarized under the term Computational Engineering, including using computational geometry and virtual design for engineering tasks,[3][4] often coupled with a simulation-driven approach[5] In Computational Engineering, algorithms solve mathematical and logical models[6] that describe engineering challenges, sometimes coupled with some aspect of AI, specifically Reinforcement Learning.[7]
In Computational Engineering the engineer encodes their knowledge using logical structuring. The result is an algorithm, the Computational Engineering Model, that can produce many different variants of engineering designs, based on varied input requirements. The results can then be analyzed through additional mathematical models to create algorithmic feedback loops.[8]
In Computational Engineering, computer simulation provides the capability to create feedback that would be inaccessible to traditional experimentation or where carrying out traditional empirical inquiries is prohibitively expensive.
Computational Engineering should neither be confused with pure computer science, nor with computer engineering,[10] although a wide domain in the former is used in Computational Engineering (e.g., certain algorithms, data structures, parallel programming, high performance computing) and some problems in the latter can be modeled and solved with Computational Engineering methods (as an application area).
Algorithms for solving discrete and continuous problems
Analysis and visualization of data
Mathematical foundations: Numerical and applied linear algebra, initial & boundary value problems, Fourier analysis, optimization
Data Science for developing methods and algorithms to handle and extract knowledge from large scientific data
With regard to computing, computer programming, algorithms, and parallel computing play a major role in Computational Engineering. The most widely used programming language in the scientific community is FORTRAN.[12] Recently, C++ and C have increased in popularity over FORTRAN. Due to the wealth of legacy code in FORTRAN and its simpler syntax, the scientific computing community has been slow in completely adopting C++ as the lingua franca. Because of its very natural way of expressing mathematical computations, and its built-in visualization capacities, the proprietary language/environment MATLAB is also widely used, especially for rapid application development and model verification. Python along with external libraries (such as NumPy, SciPy, Matplotlib) has gained some popularity as a free and Copycenter alternative to MATLAB.
Open Source Movement
There are a number of Free and Open-Source Software (FOSS) tools that support Computational Engineering.
OpenSCAD was released in 2010 and allows the scripted generation of CAD models, which can form the basis for Computational Engineering Models.
PicoGKis an open-source framework for Computational Engineering which was released under the Apache 2.0 Open-Source License in 2023 by LEAP 71, a Dubai-based company.[13]
Biology and Medicine: protein folding simulations (and other macromolecules), bioinformatics, genomics, computational neurological modeling, modeling of biological systems (e.g., ecological systems), 3D CT ultrasound, MRI imaging, molecular bionetworks, cancer and seizure control
Chemistry: calculating the structures and properties of chemical compounds/molecules and solids, computational chemistry/cheminformatics, molecular mechanics simulations, computational chemical methods in solid state physics, chemical pollution transport
