Maybe Physics-Based AI Is the Right Approach: Revisiting the Foundations of Intelligence

Over the past decade, deep learning has transformed artificial intelligence, leading to significant advancements in image recognition, language modeling, and game playing. However, persistent limitations have emerged: data inefficiency, vulnerability to distribution shifts, high energy consumption, and a limited understanding of physical laws. As AI integrates deeper into critical sectors—from climate forecasting to medicine—these constraints are becoming unacceptable.

The Case for Physics-Based AI

Why Physics, Now?

Current AI methods—particularly large language models (LLMs) and vision models—primarily extract correlations from vast, often unstructured datasets. This data-centric approach tends to underperform in scenarios characterized by data scarcity, safety-critical environments, or strict physical governance. In contrast, physics-based AI utilizes:

• Inductive Biases via Physical Constraints: By embedding symmetries, conservation laws, and invariances, the hypothesis space is reduced, guiding learning toward viable solutions.
• Sample Efficiency: Models that leverage physical priors can achieve superior results with less data, a vital asset in fields such as healthcare and computational science.
• Robustness and Generalization: Unlike traditional black-box models, physics-informed models exhibit greater reliability and fewer unexpected failures during out-of-distribution extrapolation.
• Interpretability and Trust: Predictions that conform to established laws, such as conservation of energy, yield more trustworthy and interpretable outcomes.

The Landscape of Physics-Based AI

Physics-Informed Neural Networks: The Workhorse

Physics-Informed Neural Networks (PINNs) integrate physical knowledge by penalizing deviations from governing equations (often PDEs) within the loss function. Recent developments include:

• In climate and geosciences, PINNs have achieved reliable predictions for free-surface flows with complex topography.
• In materials science and fluid dynamics, they effectively model stress distribution, turbulence, and nonlinear wave propagation.
• In biomedical modeling, PINNs have simulated cardiac dynamics and tumor progression under limited observations.

Latest Developments (2024–2025):

• Unified error analysis offers a detailed breakdown of PINN errors, emphasizing more effective training methodologies.
• Physics-informed PointNet allows PINN applications on irregular geometries without retraining for each shape.
• Next-generation PINNs incorporate multimodal architectures, combining data-driven and physics-guided elements to address partial observability and heterogeneity.

Neural Operators: Learning Physics Across Infinite Domains

Traditional machine learning models struggle with variations in physics equations and boundary conditions. Neural operators, particularly Fourier neural operators (FNOs), learn mappings within function spaces:

• In weather forecasting, FNOs have surpassed CNNs in accurately modeling nonlinear ocean and atmospheric dynamics.
• Addressing limitations such as low-frequency bias through ensemble and multiscale operator approaches has enhanced high-frequency prediction accuracy.
• Multigrid and multiscale neural operators are now leading the field in global weather forecasting.

Differentiable Simulation: Data-Physical Fusion Backbone

Differentiable simulators facilitate end-to-end optimization of physical predictions:

• In tactile and contact physics, they support learning in scenarios involving contact-rich manipulation, as well as soft-body and rigid-body physics.
• In neuroscience, they enable large-scale, gradient-based optimization of neural circuits.
• New physics engines like Genesis provide unprecedented speed and scale for simulation in learning and robotics.

Current Challenges and Research Frontiers

• Scalability: Efficiently training physics-constrained models at scale presents ongoing challenges, with advancements being made in meshless operators and simulation speed.
• Partial Observability and Noise: Managing noisy, incomplete data is a significant research focus, with hybrid and multimodal models addressing these challenges.
• Integration with Foundation Models: Research is focusing on merging general-purpose AI models with explicit physical principles.
• Verification & Validation: Ensuring models consistently adhere to physical laws across all contexts remains complex.
• Automated Law Discovery: PINN-inspired methods are making the data-driven discovery of governing scientific laws increasingly feasible.

The Future: Toward a Physics-First AI Paradigm

Transitioning to physics-based and hybrid models is not just beneficial but essential for creating AI capable of extrapolation, reasoning, and potentially discovering new scientific laws. Key future directions include:

• Neural-symbolic integration, merging interpretable physical knowledge with deep learning networks.
• Real-time, mechanism-aware AI for reliable decision-making in robotics and digital twins.
• Automated scientific discovery utilizing advanced machine learning for causal inference and law discovery.

These advancements will require robust collaboration among experts in machine learning, physics, and domain-specific fields. Rapid progress in this domain is set to unify data, computation, and domain knowledge, heralding a new generation of AI capabilities that will benefit both science and society.