By learning from examples, such as computer simulations or real-world data, artificial intelligence can help predict how light behaves in complex environments. While we use physics to understand how light travels, this process can be time-consuming and require significant computing power, especially in more complicated scenarios. AI can learn to replicate these techniques and provide responses more quickly. In fields like video games, virtual reality, and robots, where speed is important, this is useful. AI is a strong tool for knowing and forecasting how light will behave in the real world by combining smart learning with fundamental science.

Hello Farkad Adnan

Training deep-learning models on typical optics phenomena requires bypassing the computational pitfalls of the past, which their using artificial intelligence for predicting light behavior in complex environments can do. A physics-informed neural network (PINN) embeds known laws of the physics governing electromagnetism in its architecture (like Maxwell's equations), meaning it can propagate light and simulate scattering and interference on its own without solving equations directly. For example, a convolutional neural network (CNN) can analyze spatial patterns of light using diffraction data obtained from photonic crystals or subsurface-light scattering in biological tissues based on datasets generated by either simulations or measurements. In rendering pipelines, reinforcement learning optimally samples light paths and dynamically prioritizes rays contributing significantly to the final illumination (caustic or global illumination). The neural radiance fields encode density and reflectance of volumetric scene properties to synthesize photorealistic lighting effects in dynamic three-dimensional environments. Examples include LiDAR signal prediction in fog, designing a metasurface that controls light polarization, and real-time adaptive lighting for autonomous systems. The performance of AI-based methods is often poorer in precision but grander in scope than FDTD or Monte Carlo ray-tracing methods for nonlinear, dynamic, or high-dimensional cases.

AI can add meaningful value to light behavior prediction by introducing context-aware generalization rather than strict physics emulation. For instance, generative models or uncertainty-aware neural networks can predict illumination effects in dynamic environments—like fog, smoke, or reflective surfaces—where traditional models struggle due to high-dimensional variables or incomplete data. What makes AI powerful here is not just speed, but adaptability: models can be trained to infer light transport behavior across environments they haven't explicitly seen, making them suitable for robotics, surgical optics, or autonomous navigation. In my own research, explainable AI frameworks that fuse spatial features with probabilistic decision layers have helped prioritize tasks under ambiguous lighting in sensor-driven edge environments. So instead of replacing physics, AI enhances prediction under real-world constraints like noise, latency, and multi-objective tradeoffs.