equipotentiallab.
SPACE PHYSICS. COMPUTATIONAL SCIENCE.

Spacecraft charging.
Grounded in physics.

We develop TASC: an AI-driven spacecraft charging app
designed for rapid analysis and to save months of work.

Discover TASC
EXPLORE THE PHYSICS

Invisible fields.
Visible relationships.

An illustrative two-charge model.
Switch the charges to explore the contours.

FIG. 01Opposite charges. One shared field.
Charge relationship
01 / INTRODUCING TASC

From spacecraft geometry
to electrical behavior.

TASC is an AI-driven spacecraft charging app for rapid spacecraft charging analysis. It brings geometry, surface materials, electrical connections, environments, and numerical solvers into one engineering workflow.

Explore how a design charges, where differential potentials develop, and how material or electrical changes affect the result. Inspect the assumptions alongside the fields they produce.

IN ACTIVE DEVELOPMENTWindows · macOS · Linux

Keep the model in view.

Inside the TASC application

TASC Object Builder showing a prepared surface mesh with material regions and a three-dimensional geometry viewport.
01 / Prepare and inspect the computational mesh before running a charging analysis.Development interface · Open image to enlarge
TASC / AI-ASSISTED MODELING

From an image to a model
you can inspect.

A saved lander example

Keep the conversation beside the model.

Ask about geometry, materials, and the next steps in model preparation within TASC’s AI Object Builder.

TASC AI Object Builder with its chat panel on the left and a saved lander mesh on the right. An unsent example prompt asks about geometry assumptions, materials, and conductor assignments.
Actual TASC interface · Saved lander mesh with an unsent example prompt.Open full-size chat screen

Start with a reference image or a description. TASC’s AI Object Builder supports interpreting geometry, reviewing assumptions, and refining a model before accepting it for further preparation.

EXAMPLE REQUEST
“Create a conceptual lander model from this reference image. List the scale and geometry assumptions, then prepare a surface mesh for review.”
Illustrative wording for this workflow.

  1. Interpret the reference.

    Identify visible shapes and supports. Record estimates for scale, depth, and features that the image does not resolve.

  2. Build and inspect.

    In this saved example, an AI-interpreted concept was compiled locally by TASC into a 23-component lander model.

  3. Mesh, then review.

    TASC converted the concept into surface elements. An analyst still needs to review the geometry, assign materials and conductors, and approve the model before analysis.

What this example establishes

The saved artifact uses approximate geometry and an assumed 1.8 m person for scale. Hidden structure and some exterior details are omitted. Material assignments are provisional, solver input is not approved, and no charging simulation was run.

TASC-generated surface mesh of a conceptual lander, with a tapered upper body, circular platforms, supports, and landing feet.
Saved TASC output · Conceptual lander surface mesh · September 2026
Components
23
Nodes
3,077
Panels
3,720
02 / CAPABILITIES

One connected analysis.

Explicit inputs. Inspectable results.

01 / PREPARE

Build the physical model.

Create or import geometry, prepare charging surfaces, and assign materials and conductors. Keep source CAD distinct from the computational mesh.

GEOMETRY · MATERIALS · MESHING
02 / COMPUTE

Follow the charge.

Investigate surface charging, electrostatic fields, and internal dielectric charging using the supported numerical methods for each problem.

CHARGING · POTENTIAL · FIELDS
03 / UNDERSTAND

Inspect the evidence.

Review field maps, charging histories, and diagnostics. Compare configurations and retain the inputs and outputs needed to reproduce a run.

VISUALIZATION · COMPARISON · EXPORT
METHODS MATTER

Engineering insight starts
with clear assumptions.

TASC combines independent numerical methods with traceable material data. Each solver has a defined scope; verification focuses on the quantities that matter to the analysis.

Surface charging & electrostatic fields

Finite-element (MFEM) and boundary-element (BEM) methods support charging and field analysis. The documented MFEM charging path uses CPU Laplace electrostatics and analytic currents; volume plasma space charge, wakes, and plumes are outside that path’s current scope.

Internal dielectric charging

Transient internal-charging calculations connect deposited charge, electrostatics, and electrical leakage. This is a separate analysis from spacecraft surface charging, with its own physical inputs and assumptions.

Material provenance & verification

A versioned material catalog retains sources and data status. Analytical checks, convergence studies, and independent solver comparisons provide development evidence. Suitability for a particular engineering decision requires validation of that case and configuration.

03 / EQUIPOTENTIAL LAB
SPACECRAFT CHARGING · SPACE PLASMA · SCIENTIFIC SOFTWARE

Physics at the center.
Understanding as the goal.

Equipotential Lab LLC develops TASC, drawing on experience in spacecraft charging, space plasma environments, and AI. Our development priorities include independent benchmarking, reproducible analysis, and model preparation that keeps the analyst in control.

TASC is an AI-driven spacecraft charging app designed to enable rapid spacecraft charging analysis and save months of engineering work.

Explore TASC