Architecture

Physics-native AI

ActivePublished: 28 July 2026Updated: 20 September 2026Published

Key innovation

Builds the laws and constraints of physics (dynamics, conservation of energy/momentum, geometry) directly into the AI model, instead of learning them from data alone — so that predictions are physically consistent and generalize better.

How it works

1) The physics laws/constraints relevant to the domain are defined (governing equations, conservation laws, contact/collisions). 2) Physical knowledge is embedded into the model: as a loss term (PINN), as a differentiable simulator in the training loop, or as a physical environment/world model. 3) The model is trained on data (real and/or simulated) while enforcing physical constraints. 4) The resulting model makes physically consistent predictions/control and transfers better to reality (important for sim-to-real in robotics).

Problem solved

Purely data-driven models often violate the laws of physics (non-physical predictions), require huge amounts of data and generalize poorly out of distribution. Physics-native AI enforces physical consistency, improves data efficiency and prediction reliability.

Components

Physics prior/constraintsPhysical knowledge

Governing equations, conservation laws and domain constraints embedded in the model (e.g. as a loss term or hard constraints).

Official

Neural modelLearning and prediction

A network/model that learns representations and predictions consistent with the embedded physics.

Official

Differentiable simulatorPhysics in the training loop

A physics simulator through which gradients can be propagated, enabling learning "through" simulation (optional, depending on the approach).

Official

Implementation

Reference implementations

NVIDIA PhysicsNeMo (formerly Modulus)

NVIDIA

Implementation pitfalls

Differentiable-simulation costHigh

Backpropagating through physics simulation is compute- and memory-intensive.

Fix:Simplified physics models, gradient checkpointing, GPU accelerators.

Enforcing hard constraintsMedium

Soft penalties (PINN) do not guarantee strict physical compliance; hard constraints are numerically difficult.

Fix:Constraint-aware architectures, projection layers, soft/hard hybrids.

Sim-to-real gapHigh

Inaccurate simulation physics leads to errors after transfer to reality.

Fix:Domain randomization, simulation calibration, fine-tuning on real data.

Evolution

2019

Physics-informed neural networks (PINNs)

Inflection point

Raissi, Perdikaris and Karniadakis formalize PINNs — embedding governing equations into the network's loss.

2021

Differentiable physics and simulators

Differentiable simulators (learning "through" simulation) become common in robotics and scientific ML.

2024

Physics-based world models and physical AI

Physics-faithful world models and platforms (physical AI) become a foundation for robot training and sim-to-real.

Hyperparameters (configurable axes)

Physics integration methodCritical

Soft constraints (PINN loss) vs hard constraints vs differentiable simulator.

PINN lossEquations in the loss.

differentiable simLearning through simulation.

Simulation fidelityHigh

Trade-off between physics fidelity and compute cost.

Physical domainMedium

E.g. rigid-body dynamics, fluids (CFD), materials, contact.

Computational complexity

Time complexity: Zalezna od podejscia (PINN vs rozniczkowalna symulacja). Space complexity: O(model + stan symulacji).

Compute bottleneck

Differentiable simulation and constraint enforcement

Propagating gradients through physics simulation and strictly enforcing hard physical constraints are the most costly and numerically difficult parts; the sim-to-real gap adds to this.

Depends on

Wiernosc i rozdzielczosc symulacjiTwardosc ograniczen