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updated overview equations

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2021-05-09 16:45:10 +08:00
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overview.md
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@@ -108,7 +108,8 @@ observations).
No matter whether we're considering forward or inverse problem,
the most crucial differentiation for the following topics lies in the
nature of the integration between DL techniques
and the domain knowledge, typically in the form of model equations.
and the domain knowledge, typically in the form of model equations
via partial differential equations (PDEs).
Taking a global perspective, the following three categories can be
identified to categorize _physics-based deep learning_ (PBDL)
techniques:
@@ -164,7 +165,7 @@ A brief look at our _notation_ in the {doc}`notation` chapter won't hurt in both
This text also represents an introduction to a wide range of deep learning and simulation APIs.
We'll use popular deep learning APIs such as _pytorch_ [https://pytorch.org](https://pytorch.org) and _tensorflow_ [https://www.tensorflow.org](https://www.tensorflow.org), and additionally
give introductions into the differentiable simulation framework _phiflow_ [https://github.com/tum-pbs/PhiFlow](https://github.com/tum-pbs/PhiFlow). Some examples also use _JAX_ [https://github.com/google/jax](https://github.com/google/jax). Thus after going through
give introductions into the differentiable simulation framework _Φ<sub>Flow</sub> (phiflow)_ [https://github.com/tum-pbs/PhiFlow](https://github.com/tum-pbs/PhiFlow). Some examples also use _JAX_ [https://github.com/google/jax](https://github.com/google/jax). Thus after going through
these examples, you should have a good overview of what's available in current APIs, such that
the best one can be selected for new tasks.