When does AI add value to an engineering simulation project?

AI is most useful where a validated simulation already exists but evaluating the required number of designs or operating scenarios would be computationally expensive. Machine learning can create faster predictive models, while optimisation algorithms can focus simulation effort on the most promising designs. Mansim first assesses whether AI is justified rather than adding machine learning where conventional simulation would be simpler.

Do we need a large existing dataset to use AI-enabled simulation?

Not necessarily. Training data can come from existing simulations, experiments or operational measurements, or it can be generated specifically for the project. The required dataset depends on the complexity of the problem, number of input variables and required prediction range. Mansim defines the data-generation strategy during the initial technical assessment.

Can AI replace CFD or other high-fidelity simulation?

For suitable applications, AI models can replace repeated CFD evaluations once they have been trained and validated within a defined operating range. High-fidelity simulation remains important for generating training data, validating predictions and confirming final designs. Mansim generally treats AI as an acceleration layer around physics-based engineering rather than an unrestricted substitute for it.

How do you verify that an AI engineering model is reliable?

The model is tested against simulation or experimental data that was not used during training. Prediction error, physical behaviour, uncertainty and performance across the intended design space are assessed before the model is used for engineering decisions. Preferred designs can then be rechecked using high-fidelity simulation.

How much does an AI-enabled simulation project cost?

Cost depends on the availability of training data, number of design variables, complexity of the underlying simulation, required accuracy and optimisation scope. Where suitability is uncertain, the work can begin with a feasibility phase to establish whether the computational saving or additional design insight justifies development of an AI model.

Can the trained model be reused by our engineering team?

Yes, where model transfer is included in the agreed scope. Depending on the application, the deliverable can include the trained predictive model, input/output interface, supporting documentation and defined validity range. Software requirements, intellectual property and future retraining arrangements should be agreed during project scoping.

AI-Enabled Engineering Simulation and Optimisation

Mansim combines physics-based simulation with machine learning and advanced optimisation to help engineering teams explore larger design spaces, reduce computational effort and make faster, better-informed design decisions.

Our work spans physics-informed machine learning, surrogate and reduced-order models, Bayesian optimisation and AI-driven multidisciplinary design exploration. These methods complement high-fidelity CFD, thermal and multiphysics simulation rather than replacing engineering physics where it matters.

Discuss your project

See our approach

15+ years

of world-class computational simulation

250+

peer-reviewed publications underpinning our work

200+

organisations supported

25+

countries served

ISO 9001

certified quality processes

What is AI-enabled simulation?

AI-enabled simulation combines conventional physics-based modelling with machine-learning techniques that learn relationships between design inputs, operating conditions and engineering performance.

High-fidelity simulations remain essential for generating reliable physical insight, but they may be computationally expensive when hundreds or thousands of configurations must be explored. Machine-learning and reduced-order models can learn from selected high-quality simulations and then predict additional cases much faster.

This allows engineers to explore larger design spaces, identify influential parameters, optimise competing objectives and focus detailed simulation on the designs that matter most. Physics-informed approaches can also embed governing physical behaviour directly within the learning process, improving the connection between data-driven prediction and engineering reality.

Find out more

c837a6_d11c6c437c0f4feb9de8591b42ead168~mv2_edited.jpg

When is AI-Enabled Simulation Used

Large Design Spaces Are Too Expensive to Simulate

When hundreds or thousands of design combinations must be evaluated, AI models can accelerate prediction and reduce reliance on repeated high-fidelity simulations.

Multiple Design Objectives Must Be Optimised

When performance depends on competing requirements such as cooling, pressure drop, efficiency, weight, energy consumption, durability or cost.

Faster Design Iteration Is Required

When conventional simulation turnaround limits the number of concepts that can be assessed before a design, procurement or investment decision.

Existing Simulation Data Is Not Being Fully Utilised

When previous CFD, thermal, experimental or operational datasets can be converted into predictive models for future designs or operating conditions.

Real-Time or Near-Real-Time Prediction Is Needed

When full-order simulations are too computationally demanding for rapid operational decisions, control applications or digital-twin workflows.

Complex Physics Requires Better Predictive Models

When traditional correlations or lower-fidelity models cannot capture nonlinear behaviour and machine learning can be trained using high-quality simulation or experimental data.

Technical AI-Enabled Simulation Capabilities

Physics-Informed Neural Networks

PINNs combine physical governing equations with machine learning to predict engineering behaviour while retaining information about the underlying physics.

Tensor Basis Neural Networks

TBNNs can learn complex flow and heat-transfer behaviour from high-fidelity datasets and provide significantly faster predictions for suitable applications.

Surrogate and Reduced-Order Modelling

High-fidelity simulations are converted into faster predictive models for design exploration, sensitivity analysis and repeated evaluation.

Machine-Learning-Based Prediction

Neural networks and other data-driven models can predict quantities such as thermal performance, flow characteristics, particle behaviour and engineering KPIs.

Bayesian and Multi-Objective Optimisation

AI-assisted optimisation identifies promising regions of the design space while reducing the number of expensive simulations required.

Uncertainty and Sensitivity Analysis

Machine-learning workflows can help identify influential variables, quantify prediction uncertainty and determine where additional simulation or experimental evidence is most valuable.

Mansim’s research portfolio includes PINNs, TBNNs, Bayesian optimisation, ML-assisted two-phase cooling, data-driven turbulence modelling and uncertainty quantification.

Software and Simulation Ecosystem

Optimality

Multidisciplinary design analysis and optimisation for automated design-space exploration across multiple engineering objectives.

Fidelity

High-fidelity CFD and thermal-fluid simulations used to generate physical insight and training or optimisation datasets.

Celsius and Voltus

Power and thermal analysis for electronics and systems where cooling and electrical performance form part of the optimisation problem.

Sigrity, Clarity and EMX

Electromagnetic analysis that can be incorporated into multidisciplinary optimisation workflows.

Find out more about Cadence

Mansim PINN and TBNN Workflows

Research-developed machine-learning approaches for accelerating prediction of selected fluid-flow and heat-transfer problems.

Bespoke Optimisation and Reduced-Order Workflows

Project-specific workflows combining simulation data, sensitivity analysis, surrogate modelling and optimisation according to the required engineering decision

Mansim’s Approach to AI-Enabled Simulation

01

Define the engineering decision

We establish the required outputs, design variables, constraints and level of prediction accuracy.

02

Build the physics baseline

Reliable CFD, thermal, process, multiphysics or experimental data is used to define the underlying engineering behaviour.

03

Prepare the training dataset

Relevant parameters and outputs are selected and the dataset is structured for machine-learning or reduced-order modelling.

04

Train and validate the AI model

Predictions are compared against simulation or experimental results before the model is used outside the training cases.

05

Explore and optimise

The accelerated model is used for sensitivity studies, design-space exploration and optimisation.

06

Verify the preferred designs

Promising solutions are returned to high-fidelity simulation or physical evidence before final engineering recommendations are made.

Research and Technical Proof

Physics-Informed Machine Learning for Accelerated CFD

Mansim has developed physics-informed neural-network methods capable of reproducing selected CFD behaviour using trained machine-learning models. The approach is designed to reduce computational time significantly once a suitable training dataset has been developed, making it particularly relevant to parametric and optimisation studies.

The research examples presented in the Mansim catalogue include coaxial coalescence and other flow problems where PINN predictions are compared directly with CFD results.

Tensor Basis Neural Networks for Flow and Heat Transfer

Mansim has also developed Tensor Basis Neural Network approaches for predicting flow and heat-transfer behaviour. Catalogue examples compare low-resolution CFD, high-resolution CFD and TBNN predictions for turbulent-flow applications.

The objective is to retain much of the information available from computationally expensive simulations while enabling much faster evaluation during repeated design studies.

Machine Learning for Engineering Optimisation

Mansim’s wider research programme includes published work on:

Neural-network prediction and Bayesian optimisation of flow-blurring droplet size
Machine-learning optimisation of two-phase microfluidic cooling
ML-assisted prediction of nanoparticle deposition in heat exchangers
Data-driven turbulence modelling
Uncertainty and error quantification
Physics-informed neural networks for bubble growth and heat transfer

These studies provide a strong research foundation for applying machine learning to engineering simulation rather than treating AI as a standalone data-science service.

Frequently Asked Questions

AI-Enabled Simulation Projects

AI Surrogates for Molecular Adsorption: From Atoms to Design

AI-Driven Prediction of Nanoparticle Deposition for Data Centre Cooling

Optimisation of a Local Exhaust Ventilation (LEV) system

Design and optimisation of an innovative Y-Branch connection pipe to minimize head loss and waste deposition

Simulation of a Large Vertical Farming Unit

Discuss Your AI-Enabled Simulation Project

Tell us the engineering decision you need to make, the information you already have and the timescale you are working to. Your enquiry will be reviewed by a CFD engineer, who will acknowledge your enquiry within one working day and identify the most proportionate next step.