December 2025 – OPM/opm-common: Delivered TensorFlow-free neural network components and improved deployment readiness. Migrated core NN layers and scalers to NumPy with an optional TensorFlow import path during migration, and added tests to validate functionality without TensorFlow. Enabled loading of previously exported models from binary files, with licensing header updates for load_model modules. Refactored to remove EmbeddedLayers, fixed core call/forward paths, and refreshed tests to ensure reliability without TensorFlow dependencies. These changes reduce runtime dependencies, improve deployment flexibility, and provide a robust, production-ready model loading path.

In November 2025, delivered a focused configuration improvement for the Hybrid Newton method in OPM/opm-simulators. The change renames the Hybrid Newton flag parameter to improve clarity and consistency across configuration settings, reducing potential misconfigurations and easing onboarding for users configuring Hybrid Newton workflows. Implemented as a single, targeted commit (bb38a7dcd8d508e2f6fe7d9feb43f08c034f6ba8), preserving existing behavior while aligning with project naming conventions. This work enhances reliability of simulations and supports future enhancements by establishing clearer configuration semantics.

October 2025 was focused on strengthening the reliability and maintainability of the OPM/opm-simulators codebase through targeted refactors in Hybrid Newton components and a leaner test infrastructure. The work delivered clearer input tensor handling, reduced external dependencies, and streamlined test coverage to accelerate feedback and CI stability.

September 2025 monthly summary: Delivered foundational enhancements to Hybrid Newton (HyNE) configuration and expanded ML readiness, while extending NNModel support for flexible evaluation configurations. Implemented a modernization of HyNE config loading using a custom PropertyTree, standardized numeric types to Scalar, modularized configuration parsing into HybridNewtonConfig, and added Python tests for feature engineering and scaling to enable ML integration with the Flow simulator. In opm-common, added template instantiations for NNModel to support multiple evaluation configurations, broadening data handling capabilities for hybrid Newton workflows. Performed targeted stability fixes, including a small Hybrid Newton flag fix and replacing Boost with PropertyTree, and changing numeric types from double to Scalar to improve consistency. These efforts collectively improve configurability, test coverage, and readiness for ML-driven simulations, delivering tangible business value through faster experimentation, more robust configurations, and smoother Flow integration.

August 2025: Delivered Hybrid Newton ML-driven solver for FlowBlackOil in OPM/opm-simulators, enabling ML-predicted initialization to improve startup speed, accuracy, and efficiency. Implemented new configuration parameters and headers, integrated the ML flow into FlowProblem, and refactored the code to support multiple configurations with model application at specified timesteps. Added config file parsing, fluid-system validation, and initial ML test tooling to establish a testable ML workflow. Included minor fixes to stabilize integration and added the first test commits. Business value: foundational ML-assisted capabilities that set the stage for faster simulations, better stability, and scalable configurability across FlowBlackOil runs.