Lambdafields Technology for sharper imaging.

We build probabilistic inference systems for complex, real-world problems — no training data required. Our technology reconstructs reality from incomplete, noisy, and sparse observations, delivering interpretable models with quantified
uncertainties.

Whether it’s air pollution, medical imaging, or any other messy physical system: if a signal is there, we’ll find it.

REASON BEYOND NOISE.

Beyond Prediction. Towards understanding.

Accelerated MRI Reconstruction with Physics-Informed Inference.

At LambdaFields, we build models that don’t just fit data—they understand it. Our core technology is a general-purpose framework for probabilistic inference in spatiotemporal fields. It’s grounded in physics, designed for uncertainty, and built to adapt across domains.

Most systems that try to reconstruct fields from sparse or noisy data rely on black-box models or ad hoc interpolation. We do neither.

Our approach is based on Bayesian inference, where physical knowledge enters as structured priors and measurement data as likelihoods. This allows us to reconstruct fields in a way that’s interpretable, physically plausible, and quantitatively honest — including explicit uncertainty estimates.

Does this qualify as Artificial Intelligence? Yes, in the most general sense: our models perform reasoning under uncertainty, adapt over time, and infer hidden structure from incomplete data. But unlike opaque deep learning models, our methods are grounded in physics, require no mountains
of training data, and produce outputs that explain why, not just what. And while our approach stands on its own, it’s also fully interoperable with machine learning techniques—allowing for hybrid systems that combine physical insight with statistical flexibility.

Keyfeatures of the LambdaFields technology

  • 1
    Uncertainty-Aware Inference:

    Every image includes a measure of confidence. No more guessing without knowing how wrong you might be.

  • 2
    Physics-Informed Reconstruction:

    Signal models, spatial priors, and scanner configurations are baked in—not bolted on.

  • 3
    Self-Calibration:

    Automatic estimation of coil sensitivities, artifact correction, and robust reconstruction from partial data.

  • 4
    No Black Boxes:

    Our models are transparent, interpretable, and grounded in real-world physics – no hallucinations.