.gitignore

@@ -1,6 +1,3 @@
-# VSCode
-.vscode/
-
 # HPC scripts and logs
 hpc/
 

Hamiltonian.jl

@@ -68,8 +68,8 @@ end
 
 "cuTENSOR contraction and accumulation (C = A * B + C)"
 function contract_accumulate!(C::CuTensor, A::CuTensor, B::CuTensor)::CuTensor
-    cuTENSOR.contraction!(one(eltype(C)), A.data, A.inds, cuTENSOR.CUTENSOR_OP_IDENTITY, B.data, B.inds, cuTENSOR.CUTENSOR_OP_IDENTITY,
+    CUTENSOR.contraction!(one(eltype(C)), A.data, A.inds, CUTENSOR.CUTENSOR_OP_IDENTITY, B.data, B.inds, CUTENSOR.CUTENSOR_OP_IDENTITY,
-                          one(eltype(C)), C.data, C.inds, cuTENSOR.CUTENSOR_OP_IDENTITY, cuTENSOR.CUTENSOR_OP_IDENTITY)
+                          one(eltype(C)), C.data, C.inds, CUTENSOR.CUTENSOR_OP_IDENTITY, CUTENSOR.CUTENSOR_OP_IDENTITY)
     return C
 end
 

LocalPreferences.toml

@@ -1,2 +0,0 @@
-[TensorOperations]
-precompile_workload = true

Project.toml

@@ -1,7 +0,0 @@
-[deps]
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
-NVTX = "5da4648a-3479-48b8-97b9-01cb529c0a1f"
-Preferences = "21216c6a-2e73-6563-6e65-726566657250"
-TensorOperations = "6aa20fa7-93e2-5fca-9bc0-fbd0db3c71a2"
-cuTENSOR = "011b41b2-24ef-40a8-b3eb-fa098493e9e1"

README.md

@@ -1,27 +0,0 @@
-# DVR-jl
-
-Solves the quantum $n$-body problem in finite volume (lattice) with periodic boundary conditions. Uses discrete variable representation (DVR) with optional support for complex scaling to study resonances. All details can be found in [H. Yu, N. Yapa, and S. König, Complex scaling in finite volume, Phys. Rev. C 109, 014316 (2024)](https://doi.org/10.1103/PhysRevC.109.014316).
-
-Written in Julia with optional CUDA GPU acceleration (experimental).
-
-## Installation
-
-Make sure you have Julia installed. Required packages can be installed with a single command:
-```bash
-julia --project=. -e 'import Pkg; Pkg.instantiate()'
-```
-
-## Usage
-
-See `calculations/3b_bound.jl` for an example on a 3-body bound state.
-See `calculations/3b_res_from_paper.jl` for an example of a 3-body resonance via complex scaling.
-
-## Planned features
-
-- [ ] Spin and isospin degrees of freedom for nuclear calculations
-- [ ] Multi-node HPC support
-- [ ] Parity and cubic symmetries ($S_4$)
-
-## Acknowledgments
-
-The author gratefully acknowledges the guidance from Sebastian König.

calculations/EC-R2R-S.jl

@@ -1,67 +0,0 @@
-using Plots, Arpack
-
-include("../helper.jl")
-include("../Hamiltonian.jl")
-
-mode = cpu_tensor
-T = Float32 # single-precision mode
-
-V_r2(c) = r2 -> c * (-5 * exp(-r2/3) + 2 * exp(-r2/10))
-
-d = 3
-n = 2
-N = 48
-L = 30
-ϕ = pi/6
-n_imag = 1
-s = system{T}(d, n, N, L)
-
-train_cs = range(0.78, 0.45, length=5)
-train_ref = reverse([0.05387926313545913-0.008900278182520881im,
-    0.11254295298924327-0.020515067379548786im,
-    0.16060154707503538-0.03716539208626717im,
-    0.19741353362674618-0.05994519982799412im,
-    0.2219100763497223-0.08959449893439568im])
-
-extrapolate_cs = range(0.38, 0.22, length=5)
-extrapolate_ref = reverse([0.23165109150003316-0.12052751440975719im,
-    0.23190549514995962-0.1406687118589838im,
-    0.22763660218046278-0.1626190970863793im,
-    0.21807104244164865-0.18635600686249373im,
-    0.2020979906072586-0.21180157628258728im])
-
-training_E = ComplexF64[]
-training_vec = Array[]
-exact_E = ComplexF64[]
-extrapolated_E = ComplexF64[]
-
-for c in train_cs
-    println("Training c=", c)
-    H = Hamiltonian{T}(s, V_r2(c), ϕ, n_imag, mode)
-    @time evals, evecs, info = eig(H, 20, resonances = true)
-    i = nearestIndex(evals, pop!(train_ref))
-    push!(training_E, evals[i])
-    push!(training_vec, evecs[i])
-end
-
-N_EC = [sum(x .* y) for (x, y) in Iterators.product(training_vec, training_vec)]
-
-for c in extrapolate_cs
-    println("Extrapolating c=", c)
-    H = Hamiltonian{T}(s, V_r2(c), ϕ, n_imag, mode)
-    @time evals, _, info = eig(H, 40, resonances = true)
-    nearestE = nearest(evals, pop!(extrapolate_ref))
-    push!(exact_E, nearestE)
-    
-    # EC extrapolation
-    H_training_vec = H.(training_vec)
-    H_EC = [sum(x .* y) for (x, y) in Iterators.product(training_vec, H_training_vec)]
-
-    evals = eigvals(H_EC, N_EC)
-    push!(extrapolated_E, nearestE)
-end
-
-scatter(real.(training_E), imag.(training_E), label="training")
-scatter!(real.(exact_E), imag.(exact_E), label="exact")
-scatter!(real.(extrapolated_E), imag.(extrapolated_E), label="extrapolated")
-savefig("temp/EC-R2R-S.pdf")

helper.jl

@@ -1,5 +0,0 @@
-"Index of the nearest value in a list to a given reference point"
-nearestIndex(list::Array, ref) = argmin(norm.(list .- ref))
-
-"Nearest value in a list to a given reference point"
-nearest(list::Array, ref) = list[nearestIndex(list, ref)]