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Software Products & Codes by industry

Environmental risk & remediation Software & Codes

Examine contaminant sources, groundwater transport, and remediation choices. Compare uncertainty and subsurface environmental risks using transparent modeling evidence. Browse software and scientific codes relevant to this area, with methods, screenshots, and resource links.

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Choose an industry to explore relevant software and codes. Research & scientific AI brings together methods used across industries.

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EnviCloud demos organized by workflow stage, tool family, and domain.
Platforms & applicationsCloud platform

EnviCloud

  • Brings geospatial data, scientific models, and AI analyses into a shared browser workspace.
  • Provides public demonstrations spanning geothermal systems, groundwater, information theory, and resource decisions.
  • Connects maps and interactive diagnostics with reproducible model and analysis workflows.
Related contaminant source identification workflow with monitoring wells and concentration-history fits.
Platforms & applicationsEnvironmental product

ChemML

  • Uses science-informed machine learning to characterize and predict contamination processes.
  • Connects site information with tools for contaminant source interpretation, uncertainty, and remediation decisions.
  • Supports environmental teams evaluating complex geochemical behavior and management options.
Related Geysers Seismicity workflow in dark mode, showing clustered earthquake observations.
Platforms & applicationsSeismicity product

LAPIS

  • Combines seismic and injection information with physics-informed AI for induced-seismicity research.
  • Investigates stress changes and deformation associated with geothermal development and other subsurface activities.
  • Supports hazard interpretation and operational planning while keeping site evidence and uncertainty visible.
SmartTensors physics-informed machine learning and R&D 100 recognition.
Scientific frameworksOpen-source framework

SmartTensors

  • Provides a family of unsupervised, supervised, and science-informed machine-learning methods.
  • Uses matrix and tensor decompositions to extract interpretable patterns from multidimensional scientific data.
  • Includes public code and examples for feature extraction, source separation, and large scientific workflows.
MADS: Model Analysis and Decision Support.
Scientific frameworksOpen-source framework

MADS

  • Connects model calibration, sensitivity analysis, uncertainty quantification, and decision support.
  • Can analyze numerical, analytical, and machine-learning models through a common scientific workflow.
  • Provides public Julia code, documentation, and examples, alongside the historical C implementation.
Nonnegative matrix factorization with k-means clustering and physics constraints.
Matrix & tensor learningPublic scientific code

NMFk.jl

  • Extracts nonnegative latent signals and their mixing coefficients from matrix data.
  • Combines repeated factorizations, customized k-means clustering, and constraints to examine candidate signal counts.
  • Supports applications such as source separation, pattern discovery, gap reconstruction, and scientific surrogate modeling.
Three-dimensional subsurface model with geological layers and wells.
Earth-science modelsPublic scientific code

FEHM.jl

  • Provides a Julia interface to the separate FEHM flow-and-transport simulator.
  • Connects simulations of single- and multiphase flow in fractured and unfractured porous media.
  • Serves as an interface module in the MADS and ZEM frameworks; it is not the FEHM solver itself.
Related MADS parameter-calibration and model-diagnostics workflow in dark mode.
Inference & uncertaintyPublic scientific code

BIGUQ.jl

  • Applies Bayesian Information Gap Decision Theory to uncertainty and decision analysis.
  • Supports experimental-design and robustness questions within MADS workflows.
  • Includes an example comparing source-termination decisions through robustness curves.