FORCsensei at AGU19

2019-12-02 00:00:00 +0000

FORCsensei at AGU Fall 19

FORCsensei will be presented at the AGU Fall Meeting 2019. Come and see our talk:

WHEN: Monday, 9 December 2019: 16:15 - 16:30

WHERE: Moscone South, 205-206, L2

WHAT: GP14A-02: A Model Selection-Based Machine Learning Framework to Estimate Optimized First-Order Reversal Curve Distributions

FORCsensei on the go!

2019-03-14 00:00:00 +0000

FORCsensei Mobile

If you use the cloud-based version of FORCsensei, then you can process FORC data while you are on the go. Install Dropbox on your phone or tablet and use your device to launch a instance of FORCsensei on the cloud. FORCsensei will link with Dropbox so you can upload data and download results. Calculation takes place on the cloud, so you can still play Fruit Ninja while processing data!

We have lift off!

2019-03-01 00:00:00 +0000

FORCsensei Launch

FORCaist is pleased to announe the launch of the FORCsensei v1.0 package for optimized processing of FORC distributions. FORCsensei is based on Python code hosted in an interactive Jupyter Notebook. You can run the FORCsensei notebook locally (recommended) or on a free cloud service (nothing to install, but slow).

The FORCsensei Rational

FORC distributions are a powerful diagnostic tool for characterizing and quantifying magnetization processes in fine magnetic particle systems. Estimation of FORC distributions requires computation of the second-order mixed derivative of noisy magnetic hysteresis data. This operation amplifies measurement noise and for weakly magnetic systems it can compromise estimation of a FORC distribution. Several processing schemes, typically based on local polynomial regression, have been developed to produce smoothed FORC distributions that suppress detrimental noise.

As FORC analysis has become more quantitative, the smoothed distribution needs to be consistent with the measurement data from which it was estimated. This can be a challenging task even for expert users, who must subjectively adjust parameters that define the form and extent of smoothing until a satisfactory FORC distribution is obtained. For non-expert users, estimation of FORC distributions using inappropriate smoothing parameters can produce heavily distorted results corrupted by processing artifacts, which in turn can lead to spurious inferences concerning the magnetic system under investigation. FORCsensei provides a statistical machine learning framework, based on probabilistic model comparison, to guide estimation of FORC distributions. This machine learning approach will objectively select an optimal FORC distribution probabilistically, which will automate the derivative estimation process for both expert and non-expert users.