forwardmodel

A forward model, sometimes spelled forwardmodel, is a computational or mathematical model that maps a system's current state and control inputs to predicted observations or future states. It is often contrasted with an inverse model, which infers the inputs required to achieve a desired outcome or observation.

In engineering and robotics, forward models enable simulation, planning, control, and state estimation. They are used in model-based control and with filters to predict sensor readings for a given action or to forecast the next state. Forward models can be physics-based, derived from differential equations and physical laws, or data-driven, learned from examples using machine learning methods. They may be deterministic or incorporate stochastic elements to represent uncertainty.

In cognitive science and neuroscience, forward models describe how the brain predicts the sensory consequences of motor commands. Predicted outcomes are compared with actual sensory input, and discrepancies drive learning and motor correction. This framework helps explain phenomena such as motor adaptation, perception, and sensorimotor integration.

In other fields, forward models serve similar purposes for simulating physical processes. Meteorology, acoustics, geophysics, and fluid dynamics use forward modeling to forecast weather, sound propagation, seismic waves, and other evolving fields. Key characteristics include whether the model is linear or nonlinear, its level of abstraction, differentiability for optimization, and the capacity to quantify or propagate uncertainty.

Applications span planning and control, animation, virtual reality, system identification, and optimization. Common challenges include model bias, computational cost, non-stationarity of real systems, and the need to integrate data to enhance accuracy through filtering or learning.