Physics-Informed World Models: Combining PMAX, JEPA, and Energy-Based Reasoning, PINN, EBM, KONA 1.0

The intersection of deep learning and the physical sciences has reached a critical bottleneck. While standard generative architectures and traditional Physics-Informed Neural Networks (PINNs) have demonstrated early promise, their reliance on grid-level reconstruction and the dense backpropagation of physical residuals fundamentally limits their scalability, stability, and temporal extrapolation. To overcome these barriers, this discussion proposes a paradigm shift away from generative modeling and toward energy-based latent prediction, synthesizing decades of representation learning into a novel, unified physical world model: SCORE-PMAX (Stochastic Constraint-Optimized Representation Energy-based Predictability Maximization).

This architecture represents a full-circle return to the foundational principles of Predictability Maximization (PMAX) introduced in 1992, which originally established that intelligent systems should learn by predicting abstract, statistically independent latent variables rather than reconstructing raw sensory inputs. While modern Joint-Embedding Predictive Architectures (JEPAs) successfully adopted this latent-prediction philosophy, they became heavily reliant on fragile architectural heuristics, such as Exponential Moving Average (EMA) teacher networks, to prevent representation collapse. By integrating breakthroughs from the recent LeJEPA framework, SCORE-PMAX discards these heuristics in favor of explicit mathematical penalties—specifically Sketched Isotropic Gaussian Regularization (SIGReg)—to guarantee theoretically provable, non-collapsed representations of complex dynamics.

Furthermore, the SCORE-PMAX framework fundamentally transitions physical simulation from sequential, autoregressive guessing to global, non-autoregressive verification. Drawing inspiration from the KONA 1.0 Energy-Based Reasoning Model (EBRM), SCORE-PMAX evaluates entire spatiotemporal trajectories simultaneously within a continuous latent space, assigning an "energy" score based on constraint satisfaction. By injecting governing Partial Differential Equations (PDEs) as independent prior experts—a concept derived from Bayesian JEPA (BJEPA)—the physical laws act as strict energy barriers. When combined with score-guided latent diffusion samplers (D-JEPA), the system can seamlessly navigate this global energy landscape, actively denoising future states until they reach a mathematically verifiable, physically compliant configuration.

The following sections will detail how this synthesis of multimodal data assimilation (Cross-JEPA/VL-JEPA), provable latent stability (LeJEPA), and energy-based constraint satisfaction (KONA/BJEPA) establishes a robust, highly scalable standard for modeling chaotic physical reality.

Видео Physics-Informed World Models: Combining PMAX, JEPA, and Energy-Based Reasoning, PINN, EBM, KONA 1.0 канала Byte Goose AI.