What Is Computational Engineering?
Computational engineering is an interdisciplinary field that applies advanced computational methods and analysis to engineering practice. It combines engineering and physical science with applied mathematics, numerical methods, algorithms and computer programming to solve problems that are difficult to address using traditional analytical or experimental approaches.
A computational engineer develops or uses mathematical models, converts them into algorithms that computers can execute, performs simulations and analyses the resulting data. The discipline therefore sits at the intersection of engineering, mathematics and computer science. citeturn0search0turn0search4
Core Methods
Mathematical Modelling
Representing physical systems using equations, constitutive laws, conservation principles and other mathematical descriptions.
Numerical Analysis
Developing and analysing numerical methods for obtaining accurate approximate solutions to mathematical problems.
Algorithms
Designing computational procedures that transform mathematical models into efficient, reproducible calculations.
Optimisation
Finding improved designs, operating conditions or solutions subject to engineering constraints.
Uncertainty Quantification
Determining how uncertain inputs, parameters and model assumptions affect predictions.
Data Analysis
Extracting information from simulations, experiments and large scientific datasets to support engineering decisions.
Modelling and Simulation
Computer simulation allows engineers to investigate systems before constructing physical prototypes or conducting expensive experiments. High-fidelity simulations can represent fluid flow, heat transfer, structural deformation, electromagnetic fields, chemical processes and many other phenomena.
| Technique | Typical purpose |
|---|---|
| Finite Element Method (FEM) | Structural mechanics, heat transfer, solid mechanics and multiphysics problems. |
| Computational Fluid Dynamics (CFD) | Modelling fluid motion, turbulence, heat transfer and reacting flows. |
| Finite Difference Methods | Numerical solutions of differential equations on structured computational grids. |
| Finite Volume Methods | Conservation-based numerical simulation, particularly in fluid dynamics. |
| Multibody Dynamics | Simulation of interacting mechanical bodies and moving systems. |
| Monte Carlo Methods | Probabilistic simulation, uncertainty analysis and stochastic modelling. |
The LinkedIn Group Computational Mechanics Research has been created to connect the people interested in the computational mechanics field. You can also link to the LinkedIn profile Numerical Analyst.
High-Performance Computing
Many computational engineering problems require far more computing power than an individual desktop can provide. High-performance computing (HPC) uses parallel processors, large memory systems, accelerators and specialised software to perform calculations at scale.
Parallel Computing
Breaking large calculations into tasks that can be processed simultaneously across many computing cores.
Scientific Software
Developing efficient, reliable and maintainable software for numerical simulation and engineering analysis.
Large-Scale Simulation
Running high-resolution models of complex physical systems that require substantial computational resources.
Visualisation
Turning large simulation datasets into graphical representations that reveal patterns, structures and physical behaviour.
Artificial Intelligence and Machine Learning
Artificial intelligence is increasingly being combined with computational engineering. Machine-learning models can accelerate simulations, construct surrogate models, identify patterns in engineering data and assist optimisation. Scientific machine learning also seeks to incorporate mathematical and physical knowledge into data-driven models.
AI does not remove the need for engineering judgement. Reliable computational engineering requires validation, verification, appropriate physical assumptions and careful assessment of uncertainty.
Applications
Aerospace Engineering
Aircraft aerodynamics, propulsion, structural dynamics, turbulence, thermal systems and spacecraft trajectories.
Mechanical Engineering
Machine design, vibration, heat transfer, fluid systems, manufacturing and product optimisation.
Civil Engineering
Structures, geotechnical systems, water resources, transport and infrastructure resilience.
Energy Engineering
Power systems, renewable energy, nuclear engineering, batteries, thermal systems and energy optimisation.
Biomedical Engineering
Blood-flow modelling, biomechanics, medical devices, imaging and computational approaches to healthcare.
Chemical Engineering
Reaction systems, process modelling, multiphase flow, transport phenomena and process optimisation.
Environmental Engineering
Climate, pollution transport, water systems, natural hazards and environmental risk.
Materials Engineering
Predicting material behaviour, microstructure, failure, processing and advanced material design.
The Computational Engineering Workflow
A typical computational engineering investigation links physical understanding, mathematical formulation, computation and validation.
| Stage | Activity |
|---|---|
| 1. Define the problem | Identify objectives, constraints, physical processes and required outputs. |
| 2. Build the model | Translate the engineering problem into mathematical equations and assumptions. |
| 3. Discretise | Convert continuous equations into a form suitable for numerical solution. |
| 4. Implement | Develop or configure algorithms and scientific software. |
| 5. Compute | Run simulations using appropriate computing resources. |
| 6. Verify | Check that the computational implementation solves the mathematical model correctly. |
| 7. Validate | Compare predictions with experiments, observations or trusted reference solutions. |
| 8. Analyse | Interpret results, quantify uncertainty and use them to inform engineering decisions. |
Computational Engineering and Digital Twins
A digital twin is a computational representation of a physical system that can be updated with information from the real system. Computational engineering provides many of the modelling, simulation, data-assimilation and uncertainty-quantification methods needed to develop such systems.
Digital twins can support predictive maintenance, engineering design, operational optimisation and the study of complex systems over their lifecycles.
Education and Careers
Computational engineers need a combination of engineering fundamentals, mathematics, programming and computational science. Useful skills include numerical analysis, scientific programming, simulation, data analysis, optimisation and technical communication.
Engineering R&D
Develop computational models and simulation tools for new products and technologies.
Simulation Engineering
Build, run and interpret computational models of complex engineering systems.
Scientific Computing
Develop algorithms and software for large-scale scientific and engineering computation.
Data & AI
Apply machine learning and data-driven techniques to engineering problems.
Digital Engineering
Work with digital twins, virtual prototypes and model-based engineering workflows.
Research
Progress to postgraduate research in computational engineering, applied mathematics or computational science.
Computational Engineering and Computational Science
Computational engineering is closely related to computational science and engineering (CSE). CSE lies at the intersection of mathematics and statistics, computer science, and the core disciplines of science and engineering. Computational engineering focuses particularly on using those methods for engineering analysis, design and decision-making. citeturn0search1turn0search3
Further Information
- University of Texas at Austin — What Is Computational Engineering?
- UT Austin — Computational Engineering
- MIT Center for Computational Science and Engineering
- NIST — Computational Science
This webpage is a general educational overview of computational engineering. Specific methods, software and professional requirements vary by engineering discipline and application.