Physics-Informed Neural Networks (PINNs) represent a transformative approach to simulation, combining the robustness of physics-based modeling with the adaptability of machine learning. By embedding fundamental physical laws—such as conservation equations, thermodynamics, or structural dynamics—directly into the training process, PINNs ensure that every prediction adheres to established scientific principles. This integration enables PINNs to bridge the gap between traditional computational simulations and data-driven insights, achieving reliable results with reduced computational overhead.

PINNs are particularly powerful in scenarios where incomplete data or complex boundary conditions make traditional methods challenging. By leveraging their embedded physics, they can extrapolate accurate predictions even in underexplored regions of a parameter space. Engineers benefit from a dual advantage: the interpretability of physics-based approaches and the efficiency of machine learning. This capability not only enhances simulation fidelity but also accelerates design cycles, making PINNs an indispensable tool in modern engineering workflows.

MeshWorks’ Machine Learning solution transforms historical simulation data into predictive models that generate simulation-equivalent outputs in seconds. This powerful feature relies on a workflow that begins with the database creation process. Engineers can compile diverse datasets from past simulations across various physics domains, such as CFD, structural analysis, and crash simulations. These datasets are organized and optimized for efficient training.

Adaptive Trainer

The adaptive trainer utilizes this database to build and refine predictive models. The training process is visualized as a dynamic interaction where the system analyzes patterns, validates correlations, and adapts the model iteratively. This ensures that the resulting predictions maintain high fidelity to the underlying physics.

Predictor

Once the training is complete, the predictor becomes an invaluable tool for engineers. With just a few clicks, users can input new design configurations, and the predictor generates simulation-equivalent outputs almost instantaneously. This capability drastically reduces the time required for design validation and enables rapid exploration of alternative configurations. The seamless integration of the training and prediction phases makes this workflow highly efficient and user-friendly.

Engineers also benefit from a continuous feedback loop between the predictor and the adaptive trainer. As new simulation data becomes available, it can be added to the database, allowing the system to retrain and further enhance the accuracy and reliability of the predictive models. This iterative approach ensures that the Machine Learning solution evolves alongside the needs of the engineering team, making it an indispensable part of the product development cycle.

The Auto-Parameterizer in DEP MeshWorks is a game-changer, seamlessly combining over 20 years of expertise in morphing and parameterization with the power of AI/ML. This unique capability is unmatched in the market and serves as a critical differentiator, offering unparalleled value for engineers and data scientists alike.

Database Enrichment for Training Models

With the Auto-Parameterizer, engineers can easily create comprehensive Design of Experiments (DOE) from existing models with just a few clicks. For instance, if a customer has 300 designs for a particular phenomenon and requires 800 to train a predictive model accurately, the Auto-Parameterizer can generate the 500 additional designs needed to enrich the database. This ensures that the training dataset is robust and diverse, significantly improving the accuracy of the resulting machine learning model. By automating this critical step, DEP MeshWorks empowers users to build reliable predictive models efficiently and meet their specific accuracy requirements.

Accelerating Iterative Design Processes

Beyond database enrichment, the Auto-Parameterizer streamlines iterative design workflows. Once the predictor is trained, engineers can leverage the Auto-Parameterizer to rapidly explore and iterate on design variations. Historically, users of DEP MeshWorks relied on this tool to quickly generate DOE and run simulations before analyzing the output data. With the integration of AI/ML, simulation time is drastically reduced, replaced by near-instantaneous predictions within the iterative loop. This enhancement makes the entire product development process 100 to 1000 times faster, enabling teams to innovate at an unprecedented pace.

This dual functionality—enriching training datasets and accelerating iterative workflows—positions the Auto-Parameterizer as an indispensable tool for modern engineering, ensuring both efficiency and accuracy in AI/ML-driven projects.