👉 Liquid Math is a novel approach to mathematical modeling that uses deep neural networks, specifically Liquid Neural Networks (LNs), to represent and manipulate mathematical functions. Unlike traditional symbolic mathematics, which relies on rigid algebraic structures, Liquid Math employs a dynamic, compositional architecture that can adapt and learn from data. These Liquid Neural Networks are composed of various mathematical primitives (like convolutions, multiplications, and activations) combined in flexible ways, allowing them to capture complex, non-linear relationships in data with high precision. The key innovation is the ability of LNs to generate not just a single output but a sequence of outputs, each conditioned on the previous ones, effectively modeling time-series and dynamic systems. This makes Liquid Math particularly powerful for tasks like time series forecasting, control systems, and other applications requiring the modeling of evolving states.
