Home
last modified time | relevance | path

Searched refs:data (Results 1 – 19 of 19) sorted by relevance

/honee/examples/
H A Dconv_plot.py45 data = group[1]
46 data = data.sort_values('rel_error')
47 p = data['degree'].values[0]
48 h = 1 / data[res]
49 E = data['rel_error']
/honee/problems/
H A Dstg_shur14.c71 CeedScalar *wall_dist = &stg_ctx->data[stg_ctx->offsets.wall_dist]; in ReadStgInflow()
72 CeedScalar *eps = &stg_ctx->data[stg_ctx->offsets.eps]; in ReadStgInflow()
73 CeedScalar *lt = &stg_ctx->data[stg_ctx->offsets.lt]; in ReadStgInflow()
74 …CeedScalar(*ubar)[stg_ctx->nprofs] = (CeedScalar(*)[stg_ctx->nprofs]) & stg_ctx->data[stg_ctx->off… in ReadStgInflow()
100 …CeedScalar(*cij)[stg_ctx->nprofs] = (CeedScalar(*)[stg_ctx->nprofs]) & stg_ctx->data[stg_ctx->offs… in ReadStgInflow()
127 CeedScalar *phi = &stg_ctx->data[stg_ctx->offsets.phi]; in ReadStgRand()
128 …CeedScalar(*d)[stg_ctx->nmodes] = (CeedScalar(*)[stg_ctx->nmodes]) & stg_ctx->data[stg_ctx->of… in ReadStgRand()
129 …CeedScalar(*sigma)[stg_ctx->nmodes] = (CeedScalar(*)[stg_ctx->nmodes]) & stg_ctx->data[stg_ctx->of… in ReadStgRand()
193 temp_ctx->total_bytes = sizeof(*temp_ctx) + total_num_scalars * sizeof(temp_ctx->data[0]); in GetStgContextData()
203 CeedScalar *kappa = &(*stg_ctx)->data[(*stg_ctx)->offsets.kappa]; in GetStgContextData()
[all …]
H A Dsgs_dd_model.c611 …dd_temp->total_bytes = sizeof(*sgsdd_ctx) + total_num_scalars * sizeof(sgsdd_ctx->data[0]); in SgsDDContextFill()
618 …PetscCall(PhastaDatFileReadToArrayReal(comm, file_path, &sgsdd_ctx->data[sgsdd_ctx->offsets.bias1]… in SgsDDContextFill()
620 …PetscCall(PhastaDatFileReadToArrayReal(comm, file_path, &sgsdd_ctx->data[sgsdd_ctx->offsets.bias2]… in SgsDDContextFill()
622 …PetscCall(PhastaDatFileReadToArrayReal(comm, file_path, &sgsdd_ctx->data[sgsdd_ctx->offsets.out_sc… in SgsDDContextFill()
628 …PetscCall(TransposeMatrix(temp, &sgsdd_ctx->data[sgsdd_ctx->offsets.weight1], num_inputs, num_neur… in SgsDDContextFill()
635 …PetscCall(TransposeMatrix(temp, &sgsdd_ctx->data[sgsdd_ctx->offsets.weight2], num_neurons, num_out… in SgsDDContextFill()
/honee/doc/
H A Dauxiliary.md308 …aining of machine-learning models, normally uses *a priori* (already gathered) data stored on disk.
309 …the scale of the problem grows and the data that is saved to disk reduces to a small percentage of…
310 One way of working around this is to perform whatever data analysis while the simulation is activel…
311 This is known as *in situ* (in place) data analysis.
313 HONEE can facilitate *in situ* data analysis using [SmartSim](https://www.craylabs.org/docs/overvie…
314 HONEE will periodically place data into an in-memory database and a separate process can then read …
315 SmartSim is responsible for orchestrating the running of HONEE and the data-analysis processes.
318 …learning interaction with normal HPC applications, any data-analysis process can be used e.g. data…
348 The most basic functionality for *in situ* data analysis is to simply place the solution vector int…
362 - Place solution data into the smartsim database
[all …]
H A Dtheory.md431 The data-driven SGS model implemented here uses a small neural network to compute the SGS term.
445 The data-driven model parameters in the examples directory are not accurate and are for regression …
450 There are two different modes for using the data-driven model: fused and sequential.
482 - Path to directory with data-driven model parameters (weights, biases, etc.)
564 Data flow for initializing function (which creates the context data struct) is given below:
/honee/qfunctions/
H A Dstg_shur14.h39 const CeedScalar *prof_wd = &stg_ctx->data[stg_ctx->offsets.wall_dist]; in InterpolateProfile()
40 const CeedScalar *prof_eps = &stg_ctx->data[stg_ctx->offsets.eps]; in InterpolateProfile()
41 const CeedScalar *prof_lt = &stg_ctx->data[stg_ctx->offsets.lt]; in InterpolateProfile()
42 const CeedScalar *prof_ubar = &stg_ctx->data[stg_ctx->offsets.ubar]; in InterpolateProfile()
43 const CeedScalar *prof_cij = &stg_ctx->data[stg_ctx->offsets.cij]; in InterpolateProfile()
126 const CeedScalar *kappa = &stg_ctx->data[stg_ctx->offsets.kappa]; in CalcSpectrum()
155 const CeedScalar *kappa = &stg_ctx->data[stg_ctx->offsets.kappa]; in StgShur14Calc()
156 const CeedScalar *phi = &stg_ctx->data[stg_ctx->offsets.phi]; in StgShur14Calc()
157 const CeedScalar *sigma = &stg_ctx->data[stg_ctx->offsets.sigma]; in StgShur14Calc()
158 const CeedScalar *d = &stg_ctx->data[stg_ctx->offsets.d]; in StgShur14Calc()
[all …]
H A Dsgs_dd_model.h30 CeedScalar data[1]; member
42 const CeedScalar *bias1 = &sgsdd_ctx->data[sgsdd_ctx->offsets.bias1]; in DataDrivenInference()
43 const CeedScalar *bias2 = &sgsdd_ctx->data[sgsdd_ctx->offsets.bias2]; in DataDrivenInference()
44 const CeedScalar *weight1 = &sgsdd_ctx->data[sgsdd_ctx->offsets.weight1]; in DataDrivenInference()
45 const CeedScalar *weight2 = &sgsdd_ctx->data[sgsdd_ctx->offsets.weight2]; in DataDrivenInference()
59 CopyN(&sgsdd_ctx->data[sgsdd_ctx->offsets.out_scaling], (CeedScalar *)new_bounds, 12); in ComputeSgsDD_Fused()
185 CopyN(&sgsdd_ctx->data[sgsdd_ctx->offsets.out_scaling], (CeedScalar *)new_bounds, 12); in ComputeSgsDDNodal_Sequential_Outputs()
H A Dstg_shur14_type.h39 CeedScalar data[1]; // !< Holds concatenated scalar array data member
/honee/src/spanstats/
H A Dspanstats.c220 static PetscErrorCode SpanwiseStatisticsSetupDataDestroy(SpanStatsSetupData data) { in SpanwiseStatisticsSetupDataDestroy() argument
224 PetscCall(CeedElemRestrictionGetCeed(data->elem_restr_parent_x, &ceed)); in SpanwiseStatisticsSetupDataDestroy()
225 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&data->elem_restr_parent_x)); in SpanwiseStatisticsSetupDataDestroy()
226 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&data->elem_restr_parent_stats)); in SpanwiseStatisticsSetupDataDestroy()
227 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&data->elem_restr_parent_colloc)); in SpanwiseStatisticsSetupDataDestroy()
228 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&data->elem_restr_child_colloc)); in SpanwiseStatisticsSetupDataDestroy()
229 PetscCallCeed(ceed, CeedBasisDestroy(&data->basis_x)); in SpanwiseStatisticsSetupDataDestroy()
230 PetscCallCeed(ceed, CeedBasisDestroy(&data->basis_stats)); in SpanwiseStatisticsSetupDataDestroy()
231 PetscCallCeed(ceed, CeedVectorDestroy(&data->x_coord)); in SpanwiseStatisticsSetupDataDestroy()
232 PetscCall(PetscFree(data)); in SpanwiseStatisticsSetupDataDestroy()
H A Dcflpe.c178 ctx->data = spanstats; in SpanwiseStatisticsSetup_CflPe()
190 SpanStatsCtx spanstats = ctx->data; in TSMonitor_SpanwiseStatisticsCflPe()
H A Dturbulence.c201 ctx->data = spanstats; in SpanwiseStatisticsSetup_Turbulence()
213 SpanStatsCtx spanstats = ctx->data; in TSMonitor_SpanwiseStatisticsTurbulence()
/honee/examples/postprocess/
H A Dvortexshedding.py31 data=df,
37 data=df,
/honee/src/smartsim/
H A Dsolution.c42 ctx->data = smartsimsol; in TSMonitor_SmartSimSolutionSetup()
54 SmartSimSolutionData smartsimsol = ctx->data; in TSMonitor_SmartSimSolution()
/honee/
H A D.gitignore64 perf.data
H A DDoxyfile455 # with only public data fields or simple typedef fields will be shown inline in
1108 # Note that doxygen will use the data processed and written to standard output
1891 # search data is written to a file for indexing by an external tool. With the
/honee/src/
H A Dmonitor_cfl.c124 ctx->data = monitor_ctx; in SetupMontiorCfl()
139 MonitorCflCtx monitor_ctx = (MonitorCflCtx)ctx->data; in TSMonitor_Cfl()
H A Dmonitor_totalkineticenergy.c118 ctx->data = monitor_ctx; in SetupMontiorTotalKineticEnergy()
133 MonitorTotalKE monitor_ctx = (MonitorTotalKE)ctx->data; in TSMonitor_TotalKineticEnergy()
H A Dmisc.c179 int FreeContextPetsc(void *data) { in FreeContextPetsc() argument
180 if (PetscFree(data)) return CeedError(NULL, CEED_ERROR_ACCESS, "PetscFree failed"); in FreeContextPetsc()
H A Ddifferential_filter.c335 ctx->data = diff_filter; in TSMonitor_DifferentialFilterSetup()
344 DiffFilterData diff_filter = (DiffFilterData)ctx->data; in TSMonitor_DifferentialFilter()