A Hybrid Physics‐Informed Computational Framework for Adaptive Microrobotic Navigation and Causal Structure Discovery in Simulated Non‐Newtonian Environments

ABSTRACT Target optimization and autonomous navigation of untethered magnetic microrobots inside complex environments remain open challenges for computational biomedical engineering. The tumor microenvironment imposes complex physical variations, including cross‐linked extracellular matrices, anomalous subdiffusive particle transport, and shear‐thinning non‐Newtonian fluid behaviors. Classic closed‐loop control schemes utilizing explicit online estimators frequently struggle to balance structural parameter convergence with precise track retention due to conflicting excitation requirements. To address these operational constraints, this work details an integrated hybrid AI system centered on physics‐informed recurrent policy optimization. Formulating the locomotion process as a partially observable Markov decision process across an 8D observation space enables the relaxation of parameter identifiability limits in synchronous propulsion regimes through a time‐integrated Fisher information matrix methodology. Analytical evaluation indicates that a mean‐reverting Ornstein‐Uhlenbeck stochastic exploration process increases expected cumulative Fisher information to support parameter identifiability without severely disrupting path tracking fidelity. To discourage non‐physical representation states within the simulation, we implement an unrolled multi‐step dynamics loss (, ) derived from resistive force theory, bounded through a variational information bottleneck (VIB). Cross‐scale interactions between physical kinematics and biological indicators are established by a hybrid AI causal structure discovery module using W‐gated nonlinear NOTEARS and Pearl's backdoor adjustment, yielding directed acyclic graphs validated against physical simulation records. Downstream, multi‐modal spatial fields undergo log‐manifold tensor train SVD compression (TensorTME) to achieve a 3.2 data reduction at 1.24% residual error. Spatial path optimization is accelerated via a 2D Fourier neural operator (SpectralTME), which evaluates candidate target fields with a mean inference latency of 66.74 ms. Coordinated multi‐agent transport is handled through phase optimization over a 3,000‐degree‐of‐freedom artificial fluid‐structure interaction (FSI) cilia array. Evaluation across six simulated field scenarios indicates that this computational framework offers a preliminary modeling foundation by reducing computational overhead while maintaining favorable simulated navigation and magnetic‐drive performance.