Our software

Products & Scientific Code

Explore EnviTrace products and the scientific software developed by our team and collaborators. This catalog brings together our published product pages and the public SmartTensors and MADS code collections, including research software, supporting tools, and examples.

22 of 22 entries

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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.

Platforms & applicationsGeothermal product

GeoTGo

Combines geoscience data and machine learning for geothermal exploration, development, and monitoring.
Supports uses ranging from direct heating and cooling to electricity production and thermal storage.
Helps communities and project teams inspect geothermal conditions and communicate resource information.

Platforms & applicationsSubsurface product

GeoML

Applies science-informed AI to the exploration, development, and use of geologic reservoirs.
Addresses subsurface energy extraction and storage, carbon sequestration, water supply, and resource assessment.
Connects reservoir understanding with modeling and decision support across a project life cycle.

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.

Platforms & applicationsResearch prototype

UrbanAI

Analyzes environmental and socioeconomic data across neighborhoods, sites, and regions.
Combines physical models and domain expertise to investigate heat islands, microclimates, and urban energy use.
Supports research into renewable energy integration, including geothermal heating, cooling, and electricity.

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.

Platforms & applicationsReservoir product

PetroAI

Develops science-informed machine-learning models for oil and gas extraction using site data and physical models.
Accounts for reservoir complexity and geomechanical processes when examining production behavior.
Uses sensitivity and uncertainty analysis to support reservoir and operational decisions.

Platforms & applicationsResource-planning product

WEFx

Examines the connected demands and tradeoffs of water, energy, and food systems.
Supports integrated planning for water supplies, energy generation, agriculture, and resource resilience.
Uses AI and decision-analysis methods to compare alternatives rather than treating each sector in isolation.

Platforms & applicationsGeospatial platform

GAIA

Organizes and combines geospatial information for environmental, energy, and urban analyses.
Provides tools for transforming, documenting, and exploring spatial data in a geological context.
Connects spatial analysis and predictive modeling with interactive maps and visual interpretation.

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.

Scientific frameworksInformation-theory software

TensIT

Compares data representations and resolutions using retained information, structure, occupancy, and computational cost.
Investigates nonlinear dependence, redundant attributes, temporal lags, and information that emerges from combinations of variables.
Keeps uncertainty, stability, and statistical dependence separate from claims of physical causation.

Scientific frameworksQuantum-assurance research

Q-Sure

Evaluates evidence, resource budgets, and uncertainty in quantum machine-learning workflows.
Uses calibration and explicit reliability gates to support run, measure-more, reroute, or abstain decisions.
Compares frozen simulator studies with classical controls without claiming demonstrated hardware or computational quantum advantage.

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.

Scientific frameworksAnalytical simulator

WELLS

Simulates multi-well, variable-rate pumping with analytical groundwater-flow solutions.
Represents confined, unconfined, and leaky aquifers, partial penetration, wellbore storage, and boundaries.
Has C and Julia implementations and can be coupled with MADS for calibration and uncertainty analysis.

Platforms & applicationsResearch platform

DAPE

Brings Earth-science observations from different sources into a coherent analysis workflow.
Uses AI and integrated modeling to investigate relationships across complex environmental systems.
Its published concept is complemented by public examples of data preparation, feature extraction, and information analysis.

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.

Matrix & tensor learningPublic scientific code

NTFk.jl

Extracts latent factors from multidimensional arrays using nonnegative tensor factorization.
Supports CP and Tucker models, with optional sparsity and physics-informed constraints.
Examines repeated solutions and clustering stability to estimate latent feature counts along tensor dimensions.

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.

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.

Inference & uncertaintyPublic scientific code

AffineInvariantMCMC.jl

Implements the Goodman-Weare affine-invariant ensemble MCMC sampler.
Uses multiple walkers to explore Bayesian parameter distributions.
Provides a documented Julia sampling example and integration with the MADS ecosystem.

Inference & uncertaintyPublic scientific code

SVR.jl

Provides support-vector regression in Julia using libSVM.
Includes documented examples for radial-basis, linear, and polynomial kernels.
Compares fitted predictions with reference functions for regression and surrogate-modeling workflows.

Inference & uncertaintyPublic scientific code

RegAE.jl

Uses a variational autoencoder to regularize inverse problems in a latent parameter space.
Maps between normalized physical parameters and learned latent variables.
Includes optimization pathways using finite-difference or automatically differentiated gradients.

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GeoTGo

Combines geoscience data and machine learning for geothermal exploration, development, and monitoring.

Supports uses ranging from direct heating and cooling to electricity production and thermal storage.

Helps communities and project teams inspect geothermal conditions and communicate resource information.

GeoML

Applies science-informed AI to the exploration, development, and use of geologic reservoirs.

Addresses subsurface energy extraction and storage, carbon sequestration, water supply, and resource assessment.

Connects reservoir understanding with modeling and decision support across a project life cycle.

UrbanAI

Analyzes environmental and socioeconomic data across neighborhoods, sites, and regions.

Combines physical models and domain expertise to investigate heat islands, microclimates, and urban energy use.

Supports research into renewable energy integration, including geothermal heating, cooling, and electricity.

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

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.

TensIT

Compares data representations and resolutions using retained information, structure, occupancy, and computational cost.

Investigates nonlinear dependence, redundant attributes, temporal lags, and information that emerges from combinations of variables.

Keeps uncertainty, stability, and statistical dependence separate from claims of physical causation.

Q-Sure

Evaluates evidence, resource budgets, and uncertainty in quantum machine-learning workflows.

Uses calibration and explicit reliability gates to support run, measure-more, reroute, or abstain decisions.

Compares frozen simulator studies with classical controls without claiming demonstrated hardware or computational quantum advantage.

DAPE

Brings Earth-science observations from different sources into a coherent analysis workflow.

Uses AI and integrated modeling to investigate relationships across complex environmental systems.

Its published concept is complemented by public examples of data preparation, feature extraction, and information analysis.

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.