Pattern Computer Discovers Foundational Laws of Physics Directly from Raw Data, Leading the Commercial Integration of Data-Driven Equation Discovery

REDMOND, Wash., Oct. 29, 2026 (GLOBE NEWSWIRE) — Pattern Computer®, Inc. (“Pattern” or “the Company”), the global leader in Pattern Discovery, announced a historic milestone in computational physics: the successful reconstruction of the foundational Navier-Stokes equations for incompressible fluids in 2D directly from raw water flow data. Beginning with a dataset of simulated observational measurements of fluid flow dynamics over a short time period from a single set of initial and boundary conditions and, without assuming existence of a single equation governing the system, Pattern rediscovered the Navier-Stokes equation using its innovative NeuralXAI™ product. In a validation framework, the Company’s partial differential equation architecture reverse-engineered the well-established Navier-Stokes equations solely by analyzing a fluid’s vorticity profile under specific Reynolds number constraints.

The successful demo provides a vetted framework for researchers and engineers looking to model complex physical realities where governing equations do not yet exist.

By feeding its specialized Pattern Discovery Engine™ (PDE™) spatiotemporal velocity vectors, Pattern naturally isolated and mathematically formulated the continuous-medium relationships that govern fluid dynamics. Operating with no prior physical assumptions, the system independently derived the exact partial differential equation governing vorticity that has formed the bedrock of fluid mechanics since the 19th century.

Mark R. Anderson, co-founding Chair and CEO, noted: “Pattern Computer’s work represents a notable advance in data-driven scientific discovery. By regenerating the foundational Navier-Stokes equations from raw water flow data, Pattern has shown how proprietary machine learning and pattern-recognition architectures can extract the underlying physics of fluid dynamics directly from empirical data, without relying on human-derived mathematical models. By treating a known, hyper-complex physical system as a target to be learned from behavior, Pattern has verified that its tools can accurately reconstruct foundational mathematical truths without relying on predefined formulas.

“This architecture serves as a critical proof of concept for automated physics discovery. We are not claiming to advance fluid dynamics or introduce new physics; the Navier-Stokes equations are already known to science. Rather, the significance lies in two critically important arenas: first, using a neural network to input physical data, and then prove our ability to break through the “black box” and deliver true explainability, using our NeuralXAI product; and, second, validation. By successfully reconstructing an established, chaotic system from raw observation, we can benchmark our AI’s output against scientific reality to confirm it identifies genuine physical laws instead of superficial data correlations.”

“Having validated this data-driven methodology on a known system, the logical next step is deployment against the unknown,” Anderson added. “This benchmark proves that under real-world, continuous conditions, our architecture can reliably turn raw empirical data into a computationally tractable, immediately actionable engineering tool. If applied to less understood or unmapped continuous systems, this same technique can be extrapolated to discover entirely new governing equations that are currently hidden from science.”

Democratizing and Commercializing the Physics-AI Revolution
While much of the artificial intelligence landscape focuses on high-profile, theoretical milestones — such as deploying massive compute power to trace the exact smoothness and breakdown behaviors of complex fluid equations — Pattern prioritizes immediate, real-world utility. Rather than chasing abstract mathematical proofs through hyper-scaled architecture, the Company developed a proprietary pipeline that maps partial differential equations directly from noisy datasets, delivering practical insights without the need for resource-heavy compute swarms.

Unlike traditional deep learning / large language models that operate as “black boxes” — predicting fluid movement without explaining the underlying physics — this highly efficient pipeline bridges the gap between raw observation and fully interpretable, human-readable symbolic algebra. Instead of chasing abstract mathematical proofs regarding how fluids behave at their theoretical limits, this technology rapidly distills one of the governing laws into transparent, actionable formulas that others can immediately understand, verify, and put to practical use.

About Pattern
Pattern Computer, Inc. is a next-generation AI platform company that uses its Pattern Discovery Engine™ to solve important and intractable problems in business and medicine. Its proprietary mathematical techniques in advanced AI identify complex patterns in very-high-order data that have eluded detection by much larger systems, including LLMs. As the Company applies its computational platform to drug discovery and diagnostics, it is also making major Pattern Discoveries for partners in sectors including extended biotech, climate challenges and materials science, aerospace manufacturing quality control, veterinary medicine, air traffic operations, equity trading, AI regulatory compliance in the EU, and energy services. See www.patterncomputer.com.

CONTACT: Laura Guerrant-Oiye (808) 960-2642 – laura@patterncomputer.com

The foregoing contains statements about Pattern Computer’s future that are not statements of historical fact. These statements are “forward looking statements” for purposes of applicable securities laws and are based on current information and/or management’s good faith belief as to future events. The words “believe,” “expect,” “anticipate,” “project,” “should,” “could,” “will,” and similar expressions signify forward-looking statements. Forward-looking statements should not be read as a guarantee of future performance. By their nature, forward-looking statements involve inherent risk and uncertainties, which change over time, and actual performance could differ materially from that anticipated by any forward-looking statements. Pattern Computer undertakes no obligation to update or revise any forward-looking statement.

Copyright © 2026 Pattern Computer Inc. All Rights Reserved. Pattern Computer, Inc., Pattern Discovery Engine, PatternBio, TrueXAI, and ProSpectral are trademarks of Pattern Computer Inc. or its subsidiaries. Other trademarks may be trademarks of their respective owners.

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