A Fully Analog Continuous-Time CIM Neural ODE Solver for Flow-Matching-Based Fluid Dynamics Generation

The generation of fluid-dynamics fields is essential for understanding complex nonlinear systems and enabling real-time scientific computing. Conventional computational fluid dynamics pipelines rely on finite-element or finite-volume solvers on von Neumann architectures, which discretize continuous physical evolution into many iterative updates, leading to prohibitive latency and energy consumption. Inspired by neural dynamical systems in the brain, we propose a biologically inspired continuous-time hardware-software co-design framework for flow-matching-based turbulent-flow generation. (1) The flow-matching model adopts an MLP-Mixer architecture that emulates cortical-style information integration and hierarchical signal mixing, providing a compact backbone that naturally aligns with closed-loop analog computation. (2) A fully analog continuous-time RRAM CIM neural ordinary differential equation (ODE) solver is developed to physically realize neural-like continuous-time latent dynamics, enabling high-speed and low-power flow generation. (3) Noise-aware training and decoder retraining are jointly introduced to ensure robust generation quality in the presence of RRAM read/write noise. Experiments on three turbulent-flow datasets show that the MLP-Mixer backbone matches convolutional and attention-based flow-matching models in velocity-field accuracy while mapping efficiently to CIM hardware, and that the proposed analog ODE solver reduces energy consumption by 98.24% and latency by 99.99% compared with an NVIDIA A100 GPU, while maintaining stable generation fidelity under realistic RRAM read/write noise. This work establishes a new paradigm for high-speed, energy-efficient physical process generation and scientific AI acceleration using neuromorphic continuous-time CIM computing.