Physics AI is a powerful engineering tool based on a foundation of digital transformation.
Physics AI is the relatively new fusion of generative artificial intelligence (AI) software and the laws of physics embedded in AI models. In their work, engineers are continuously aware of and constrained by the laws of physics. This fusion can advance both their routine work and experimentation, leading to innovation.
Digital transformation is the inescapable prerequisite for physics-driven AI. It provides sufficient digital data, more functional and integrated software, and consistent, accelerated digital workflows. Physics AI is visible not only in advanced research institutions but also in the industries that rely on complex physical systems, from energy to aerospace to materials engineering.
Physics AI is the term applied to the two situations where physics and AI advance the application of both:
- AI techniques are applied to solve complex physics problems, often in ways that were previously infeasible.
- Known physical laws are applied to enhance the accuracy and robustness of AI models, thereby improving their responses.
Physics AI is reshaping how researchers and engineers build models, validate theories, and incorporate physical principles into complex numerical applications, such as simulations of digital twins, fluid dynamics turbulence, quantum-mechanical interactions, and structural deformation. Traditional simulations are computationally expensive, limiting the number of scenarios that engineers can run and evaluate. Physics AI is adding a new dimension to today’s digital data-rich, computationally intensive engineering environment.
Unlike general AI models, physics AI models must operate within the constraints, structure, and mathematical rigour imposed by physical laws. Engineers and data scientists design physics AI models to integrate fundamental principles such as conservation laws, symmetry, empirical observation and causality into their architectures, training processes, validation, and deployment. By contrast, general AI models are optimized to detect statistical patterns in large volumes of data for a broader range of applications.
Physics AI models differ markedly from general AI models in the following crucial ways. The specific application characteristics will determine which type of model is most suitable for a given engineering application.
Inductive bias
The most prominent distinction between the two types of models lies in inductive bias. Inductive bias is the set of assumptions a model makes to generalize from a limited training dataset to new data it has not previously seen.
Physics AI models embed established equations rather than large volumes of data and make few, if any, assumptions. The equations are often partial differential equations (PDEs) or analytical constraints that the models apply directly in the optimization process. This approach, seen in physics-informed neural networks (PINNs) and related architectures, ensures solutions remain physically plausible even when the training dataset is sparse or modest.
General AI models learn patterns solely from data and may produce outputs that violate known physical laws if the training dataset is noisy, contradictory, biased, or incomplete. These data issues can lead to assumptions that create hallucinations in the output.
Data efficiency
A second difference involves data efficiency. Data efficiency measures how little data a model requires to achieve reliable, verifiable output. Data efficiency is essential when dealing with the many physical systems that are difficult or expensive to measure.
Physics AI models offer a significant advance for large physical systems by fusing theoretical models with modest amounts of empirical data, often sensor time series data, to achieve accurate predictions. Examples include modeling of weather, plasma physics, molecular interactions, materials science, and astrophysics.
General AI models struggle when asked about large physical systems because the vast volumes of data required to produce reliable output don’t exist and would be difficult for the model to ingest, even if they did. For the same reason, synthetic data generation techniques are not feasible.
Interpretability and verifiability
Another distinguishing feature is the emphasis on interpretability and verifiability. Interpretability is the degree to which engineers can understand the internal mechanics and decision-making processes of an AI system. Verifiability is the ability to ensure that an AI system is operating as intended.
Physics AI models are particularly valuable for knowledge domains where scientific validity is crucial. In such applications, predictions must be verifiable in terms of values for variables such as energy transfer, force interactions, boundary conditions, or geometric constraints. Physics AI models are thus often designed to produce values for intermediate variables that relate directly to physical quantities.
General AI models, especially those with deep learning architectures, are usually not interpretable in detail without specialized interpretation tools.
Generalization
Additionally, the concept of generalization behaves differently in the two types of models. A generalization is a form of abstraction in which common properties of specific instances hold for all or almost all other instances.
General AI models may struggle when encountering out-of-distribution data and assume a generalization applies when it does not. This situation creates hallucinations in the output.
Physics AI models often generalize better because the underlying physical laws are universal. Embedding these laws effectively reduces the extent of the knowledge domain, allowing the model to extrapolate beyond the scope of the training data while remaining within the domain with greater reliability.
Evaluation criteria
Finally, the evaluation criteria for acceptable output diverge for the two types of models. Evaluation criteria are the standards or benchmarks used to assess the quality, technical capability, performance, and suitability. Some of the many examples include fit for purpose, plausibility, accuracy of the output, and a low error term in the results.
In physics, the accuracy of AI output is judged not only by statistical measures such as cost, vibration, or error terms, but also by adherence to conservation principles, stability conditions, and numerical consistency.
Statistical measures do not bind a general AI model. It may, for example, predict realistic physical trajectories that violate conservation of momentum without recognizing their impossibility. The model’s output is typically evaluated based on its plausibility and congruence with other results.
In summary, physics AI models differ by incorporating physical theory into their structure, enabling higher data efficiency, stronger generalization, and stricter scientific validity than general-purpose AI models. These differences make physics AI models attractive to engineers for their work. It’s time to experiment with physics AI models, especially those offered by various vendors.
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