Reference Summary: Wolfgang Waltenberger describes the SmodelS package, for fitting simplified theory models to data. David Straub looks at the use of Python in the HEP Theory community as part of the

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As pyhf continues to be developed and as the user community has grown significantly, both in users and in subfields of David Straub looks at the use of Python in the HEP Theory community as part of the

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A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Wolfgang Waltenberger describes the SmodelS package, for fitting simplified theory models to data.

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  • As pyhf continues to be developed and as the user community has grown significantly, both in users and in subfields of
  • Wolfgang Waltenberger describes the SmodelS package, for fitting simplified theory models to data.
  • A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via
  • David Straub looks at the use of Python in the HEP Theory community as part of the

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PyHEP 2020 Physics Analysis as a Differentiable Program
PyHEP2022 Analysis Optimisation with Differentiable Programming
PyHEP 2020 pyhf Tutorial
Differentiable Programming in HEP
PyHEP 2020 Python and HEP, a perfect match, in theory
Differentiable programming for particle physics simulations (R. Grinis)
A First Principles Approach for System Identification via Differentiable Physics Engines (L4DC2020)
PyHEP 2020 SmodelS
Peter Battaglia - Exploiting differentiability for learning and prediction
pyhf tutorial and exploration
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PyHEP 2020 Physics Analysis as a Differentiable Program

PyHEP 2020 Physics Analysis as a Differentiable Program

Read more details and related context about PyHEP 2020 Physics Analysis as a Differentiable Program.

PyHEP2022 Analysis Optimisation with Differentiable Programming

PyHEP2022 Analysis Optimisation with Differentiable Programming

Read more details and related context about PyHEP2022 Analysis Optimisation with Differentiable Programming.

PyHEP 2020 pyhf Tutorial

PyHEP 2020 pyhf Tutorial

Matthew Feickert gives a tutorial on using pyhf for accelerating

Differentiable Programming in HEP

Differentiable Programming in HEP

Read more details and related context about Differentiable Programming in HEP.

PyHEP 2020 Python and HEP, a perfect match, in theory

PyHEP 2020 Python and HEP, a perfect match, in theory

David Straub looks at the use of Python in the HEP Theory community as part of the

Differentiable programming for particle physics simulations (R. Grinis)

Differentiable programming for particle physics simulations (R. Grinis)

Read more details and related context about Differentiable programming for particle physics simulations (R. Grinis).

A First Principles Approach for System Identification via Differentiable Physics Engines (L4DC2020)

A First Principles Approach for System Identification via Differentiable Physics Engines (L4DC2020)

A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via

PyHEP 2020 SmodelS

PyHEP 2020 SmodelS

Wolfgang Waltenberger describes the SmodelS package, for fitting simplified theory models to data. Part of the

Peter Battaglia - Exploiting differentiability for learning and prediction

Peter Battaglia - Exploiting differentiability for learning and prediction

Read more details and related context about Peter Battaglia - Exploiting differentiability for learning and prediction.

pyhf tutorial and exploration

pyhf tutorial and exploration

As pyhf continues to be developed and as the user community has grown significantly, both in users and in subfields of