Pattern Computer Releases Demonstration: Navier – Stokes Equation from Raw Data

Provides Vetted Framework for Unmapped Realities across Tech and Life Sciences Industries

REDMOND, Wash., Nov. 10, 2026 (GLOBE NEWSWIRE) -- Pattern Computer, Inc. (“Pattern” or “the Company”), the next-generation leader in advanced Pattern Discovery, today announced the release of a comprehensive video demonstration showcasing a breakthrough in automated physics discovery.

The video illustrates using the company's success in resolving two of the top challenges in compute today: using its NeuralXAI™ product to break the "black box" problem in neural networks and using its Pattern Discovery Engine™ (PDE™ and PatternDE™) to reconstruct the foundational 2D Navier-Stokes equations for incompressible fluids directly from raw, unmapped water flow data. While foundational language model companies are struggling over whether they have actually resolved a related mathematical puzzle, Pattern has shown it can produce the entire source equation in 30 minutes, vs. its original 28-year origination... The video is here: VI Navier Stokes Demo Part 1 & 2 | Videos & Movies on Vimeo

This video release provides a vetted, actionable framework for researchers and engineers looking to mathematically model complex physical realities where governing equations do not yet exist. By demonstrating how a neural network can ingest raw spatiotemporal data, break through the traditional "black box" of neural networks, and output human-readable, transparent symbolic algebra, Pattern has been establishing new benchmarks for true explainability in computational science soon after its inception. The implications of this vetted framework extend far beyond fluid mechanics, driving a paradigm shift across the broader technology and life sciences landscapes.

This validated data-to-formula pipeline operates with a low-compute footprint, eliminating the massive infrastructure overhead and environmental costs of traditional distributed compute swarms. By replacing opaque statistical guesswork with transparent symbolic equations, Pattern delivers measurable commercial advantages across highly regulated verticals.

Strategic Application in Enterprise Technology: Replacing Black-Box AI with Explainable Engineering
For enterprise technology executives, Pattern’s architecture transforms machine learning from a speculative, correlation-based forecasting method into a rigorous tool for deterministic engineering. By extracting exact governing formulas directly from noisy datasets, this framework eliminates the uninterpretable "black box" risks that currently stall generative and deep learning models from mission-critical deployments. Operating without the need for resource-heavy compute swarms or predefined baseline equations, this low-footprint architecture allows technology companies to reliably turn raw empirical data into computationally tractable, legally defensible, and immediately actionable assets across highly regulated verticals:

• De-Risking Mission-Critical Operations: Replaces opaque statistical predictions with transparent, human-readable symbolic equations, satisfying strict global standards such as the EU AI Act compliance frameworks.
• Maximizing Compute Efficiency: Eliminates the massive overhead, cost, and infrastructure dependencies of traditional distributed compute swarms, yielding a high-fidelity extraction pipeline with minimal processing requirements.
• Driving High-Stakes Verticals: Deploys immediate engineering tools for hyper-complex systems, ranging from climate challenges and materials science, aerospace manufacturing quality control, veterinary medicine, equity trading, AI regulatory compliance in the EU, energy services and more.
  
  

Strategic Application in Biopharma: Accelerating Target Identification and Pipeline Velocity
For biopharmaceutical executives, Pattern’s architecture transforms the industry's approach to complex, unmapped biological systems from high-risk predictive modeling into deterministic, explainable engineering. By converting massive, chaotic clinical and empirical datasets into transparent, verifiable governing formulas, this validated framework eliminates the traditional "black box" risk of legacy AI. Biopharma R&D teams can leverage this direct data-to-formula pipeline to unlock highly efficient pathways across the entire drug development lifecycle:

• De-Risking Discovery: Translates raw clinical and cellular observations into human-readable symbolic equations, providing a transparent, scientifically sound mechanism to validate mechanisms of action early.
• Optimizing Pipeline Velocity: Discovers explicit mathematical laws governing real-time metabolic pathway flux, complex cellular dynamics, and precise drug-target binding interactions to dramatically shorten the hit-to-lead timeline.
• Precision Targeting: Empowers researchers with exact, predictive formulas to model non-linear genomic expressions and multi-omics data, accelerating the delivery of high-efficacy therapeutics and advanced diagnostics.
  
  

“Our successful reconstruction of a known, chaotic physical system serves as a great validation for our architecture," said Mark R. Anderson, co-founding Chair and CEO of Pattern Computer. "Now that we have benchmarked our AI's output against established scientific reality to prove it identifies genuine physical laws instead of superficial correlations, we can continue our deployment against the unknown. This video demo shows the world exactly how we can extrapolate this technique to discover entirely new governing equations that are currently hidden from science, altering how both tech and life sciences companies solve their most intractable problems."

Mr. Anderson continued, “Our architecture fundamentally shifts the enterprise language model AI paradigm from speculative statistical correlation to absolute mathematical certainty. For executives in highly regulated sectors like tech and biopharma, legacy black-box AI introduces unacceptable operational, clinical, and regulatory liabilities. By extracting exact, transparent governing formulas directly from chaotic data — without the burden of massive compute swarms — we are turning raw data into legally defensible, revenue-driving engineering tools. We are not just accurately predicting system behaviors; we are giving enterprise leaders the exact mathematical blueprints required to de-risk outcomes, satisfy strict global frameworks like the EU AI Act, make major new discoveries vs. incremental improvements, and collapse R&D timelines from years to weeks.”

About Pattern
Pattern Computer, Inc. is a next-generation AI/ML 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 and the world’s largest supercomputers.. As the Company applies its computational platform to drug discovery and diagnostics, it is also making major Pattern Discoveries for partners in sectors including diagnostics and therapeutics for the world’s top five cancers, realtime screening for known diseases and bio-attack, clinical pathology tools, new materials discovery, aerospace manufacturing quality control, equity trading, AI regulatory compliance in the EU, energy services and more. See www.patterncomputer.com.

CONTACT: Denyse Hudson – denyse@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.

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