Particle physics and various methods related to manifold understanding.
Process the data from a particle physics experiment, such as in advance of anomaly detection.
POST /processing/physics/collider_experiment
Name | Type | Description | Required |
---|---|---|---|
manifest | string | GCS bucket path for GCS file path manifest | ✔️ |
Status: 200
{
"task_identifier": "EFTGHDFER"
}
curl -X POST https://api.rvrnc.io/processing/physics/collider_experiment \
--data '{
"manifest": "gs://user/manifest.csv"
}'
Perform unsupervised learning on a large number of manifold representations, providing a taxonomy.
POST /processing/physics/manifold_understanding
Name | Type | Description | Required |
---|---|---|---|
manifest | string | GCS bucket path for GCS file path manifest | ✔️ |
Status: 200
{
"task_identifier": "EFTGHDFER"
}
curl -X POST https://api.rvrnc.io/processing/physics/manifold_understanding \
--data '{
"manifest": "gs://user/manifest.csv"
}'
Process the trace of the dynamics of a physical phenomenon, i.e. a time-series, for the purpose of learning to better understand the phenomenon from data or learn a faster implementation of an existing dynamics modelling tool.
POST /processing/physics/process_dynamics
Name | Type | Description | Required |
---|---|---|---|
manifest | string | GCS bucket path for GCS file path manifest | ✔️ |
Status: 200
{
"task_identifier": "EFTGHDFER"
}
curl -X POST https://api.rvrnc.io/processing/physics/process_dynamics \
--data '{
"manifest": "gs://user/manifest.csv"
}'