{"push_id":1155689438,"size":3,"distinct_size":3,"ref":"refs/heads/master","head":"eace1331380dfb2e36a741cc48b31d110102740c","before":"78f8cf38948268ee960f5a733113f25b7ca05fa0","commits":[{"sha":"1dcb2482da88cc6e89518ec8909e7d73f2a09733","author":{"name":"Andreas Lauser","email":"e112853876b59705235362b89df80258ee9c047a@poware.org"},"message":"rename \"LocalAd\" to \"DenseAd\"\n\nSince \"dense automatic differentiation\" describes what this code is\nall about much better in my opinion. (\"Local AD\" is just a possible\nuse case in the context of PDE discretization.)","distinct":true,"url":"https://api.github.com/repos/pgdr/opm-material/commits/1dcb2482da88cc6e89518ec8909e7d73f2a09733"},{"sha":"ffe7bbd1b26985f3a36195e8b54b9a7e27441c73","author":{"name":"Andreas Lauser","email":"e112853876b59705235362b89df80258ee9c047a@poware.org"},"message":"rename MathToolbox::toLhs to MathToolbox::decay\n\nthis is because this method is completely analogous to std::decay()...","distinct":true,"url":"https://api.github.com/repos/pgdr/opm-material/commits/ffe7bbd1b26985f3a36195e8b54b9a7e27441c73"},{"sha":"eace1331380dfb2e36a741cc48b31d110102740c","author":{"name":"Andreas Lauser","email":"e112853876b59705235362b89df80258ee9c047a@poware.org"},"message":"Merge pull request #147 from andlaus/localad_to_densead\n\nrename \"LocalAd\" to \"DenseAd\"","distinct":true,"url":"https://api.github.com/repos/pgdr/opm-material/commits/eace1331380dfb2e36a741cc48b31d110102740c"}]}
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