From e3dd37cfb12f15f0d46b0246da4af2ee9e81faec Mon Sep 17 00:00:00 2001 From: Felix Koehler Date: Wed, 10 Apr 2024 09:30:33 +0200 Subject: [PATCH] Add logo --- README.md | 34 +++++++++++++++++++++------------- exponax_logo.png | Bin 0 -> 119280 bytes 2 files changed, 21 insertions(+), 13 deletions(-) create mode 100644 exponax_logo.png diff --git a/README.md b/README.md index 5514414..9380493 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@

+ +
Exponax

@@ -59,20 +61,26 @@ plt.xlabel("Time"); plt.ylabel("Space"); plt.show() ## Features -Using JAX as the computational backend gives: - -1. **Backend agnotistic code** - run on CPU, GPU, or TPU, in both single and double - precision. -2. **Automatic differentiation** over the timesteppers - compute gradients of - solutions with respect to initial conditions, parameters, etc. -3. Also helpful for **tight integration with Deep Learning** since each - timestepper is also just an [Equinox](https://github.com/patrick-kidger/equinox) Module. -4. **Automatic Vectorization** using `jax.vmap` (or `equinox.filter_vmap`) - allowing to advance multiple states in time or instantiate multiple solvers at a time that operate efficiently in batch. - -Exponax strives to be lightweight and without custom types; there is no `grid` or `state` object. Everything is based on `jax.numpy` arrays. -**Other functionality** +1. **JAX** as the computational backend: + 1. **Backend agnotistic code** - run on CPU, GPU, or TPU, in both single and + double precision. + 2. **Automatic differentiation** over the timesteppers - compute gradients + of solutions with respect to initial conditions, parameters, etc. + 3. Also helpful for **tight integration with Deep Learning** since each + timestepper is also just an + [Equinox](https://github.com/patrick-kidger/equinox) Module. + 4. **Automatic Vectorization** using `jax.vmap` (or `equinox.filter_vmap`) + allowing to advance multiple states in time or instantiate multiple + solvers at a time that operate efficiently in batch. +2. Lightweight design without custom types. There is no `grid` or `state` + object. Everything is based on `jax.numpy` arrays. Timesteppers are callable + PyTrees. +3. More than 35 pre-built dynamics: + 1. Linear PDEs in 1d, 2d, and 3d (advection, diffusion, dispersion, etc.) + 2. Nonlinear PDEs in 1d, 2d, and 3d (Burgers, Kuramoto-Sivashinsky, + Korteweg-de Vries, Navier-Stokes, etc.) + 3. Reaction-Diffusion Next to the timesteppers operating on JAX array states, it also comes with: diff --git a/exponax_logo.png b/exponax_logo.png new file mode 100644 index 0000000000000000000000000000000000000000..269d9b95046ba2dea0f6b662f90e97d30a608326 GIT binary patch literal 119280 zcmeFYWmH_v(l(5{dyoKw`v8Ls?(PuWVQ_Z~?(QMDI|K^^x8MX15Hz?u!3i(9k39ET z-}~oT>wEv5nKiTabXQ$fb#-;^-Fx>$sVGUKqY$G&K|!I*%1Ed|K|znb9zP%79JnW%?_^< zvqf7_{HHU@6!Vl>GqWP>9|p{{oK!Gf9RoowdjY>6+g>)cs=#gfx}SjShyG3IXha<3MD^#aV{WV4;}H` zVe>RxUpTp4xV;UofAKCj!!15hi;AHU{rv~=&M{%~9J`{-p} zPoZ%aSQ9#y6E`#8S)@s__V-G}(>EM)z`cf(T-Tst_Rw$0Va z)^W?@a`E1WWwmqrX(#MMr_^r`6G?`_BFsy!k~6iGfDhScg;&o^gdQt;w}(FCb4E7; zzpw;76>iY)9+EB(UWk9@1w5SLKHnd$M}13}`tl-g_jLZ7hT%n#>{+K*xcjjvxc&J( zbURnKqii=}#wX#Ir{x9ldph|0(Ni|hHQ}z9?rx=>+I5(R8gLog?WzBIq0Yc~+V;X( zp+ED*`=Li|ZKboKwDq_b)ODV9QX_=!_B{V-gk?6LwLAY%88^palZZDCyl0d61PK{i zQE|d%`s|$x<;Mil`sP%Lg#HmEB(2Nl9HqZd*vsy&bo$y)yjt`MT(`))EYaet;Ds*9 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