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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,49 @@ | ||
| struct LambaEMConstantCache <: StochasticDiffEqConstantCache end | ||
| struct LambaEMCache{uType,rateType,rateNoiseType} <: StochasticDiffEqMutableCache | ||
| u::uType | ||
| uprev::uType | ||
| du1::rateType | ||
| du2::rateType | ||
| K::rateType | ||
| tmp::uType | ||
| L::rateType | ||
| gtmp::rateNoiseType | ||
| end | ||
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| u_cache(c::LambaEMCache) = () | ||
| du_cache(c::LambaEMCache) = (c.du1,c.du2,c.K,c.L) | ||
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| alg_cache(alg::LambaEM,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,uEltypeNoUnits,uBottomEltype,tTypeNoUnits,uprev,f,t,::Type{Val{false}}) = LambaEMConstantCache() | ||
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| function alg_cache(alg::LambaEM,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,uEltypeNoUnits,uBottomEltype,tTypeNoUnits,uprev,f,t,::Type{Val{true}}) | ||
| du1 = zeros(rate_prototype); du2 = zeros(rate_prototype) | ||
| K = zeros(rate_prototype); tmp = similar(u); | ||
| L = zeros(noise_rate_prototype) | ||
| gtmp = zeros(noise_rate_prototype) | ||
| LambaEMCache(u,uprev,du1,du2,K,tmp,L,gtmp) | ||
| end | ||
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| struct LambaEulerHeunConstantCache <: StochasticDiffEqConstantCache end | ||
| struct LambaEulerHeunCache{uType,rateType,rateNoiseType} <: StochasticDiffEqMutableCache | ||
| u::uType | ||
| uprev::uType | ||
| du1::rateType | ||
| du2::rateType | ||
| K::rateType | ||
| tmp::uType | ||
| L::rateType | ||
| gtmp::rateNoiseType | ||
| end | ||
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| u_cache(c::LambaEulerHeunCache) = () | ||
| du_cache(c::LambaEulerHeunCache) = (c.du1,c.du2,c.K,c.L) | ||
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| alg_cache(alg::LambaEulerHeun,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,uEltypeNoUnits,uBottomEltype,tTypeNoUnits,uprev,f,t,::Type{Val{false}}) = LambaEulerHeunConstantCache() | ||
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| function alg_cache(alg::LambaEulerHeun,prob,u,ΔW,ΔZ,p,rate_prototype,noise_rate_prototype,uEltypeNoUnits,uBottomEltype,tTypeNoUnits,uprev,f,t,::Type{Val{true}}) | ||
| du1 = zeros(rate_prototype); du2 = zeros(rate_prototype) | ||
| K = zeros(rate_prototype); tmp = similar(u); | ||
| L = zeros(noise_rate_prototype) | ||
| gtmp = zeros(noise_rate_prototype) | ||
| LambaEulerHeunCache(u,uprev,du1,du2,K,tmp,L,gtmp) | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,178 @@ | ||
| @muladd function perform_step!(integrator,cache::LambaEMConstantCache,f=integrator.f) | ||
| @unpack t,dt,uprev,u,W,p = integrator | ||
| du1 = integrator.f(uprev,p,t) | ||
| K = @muladd uprev + dt*du1 | ||
| L = integrator.g(uprev,p,t) | ||
| mil_correction = zero(u) | ||
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| u = K+L*W.dW | ||
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| if integrator.opts.adaptive | ||
| du2 = integrator.f(K,p,t+dt) | ||
| Ed = dt*(du2 - du1)/2 | ||
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| utilde = K + L*integrator.sqdt | ||
| ggprime = (integrator.g(utilde,p,t).-L)./(integrator.sqdt) | ||
| En = ggprime.*(W.dW.^2 .- dt)./2 | ||
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| integrator.EEst = integrator.opts.internalnorm((Ed + En)/((integrator.opts.abstol + max.(abs(uprev),abs(u))*integrator.opts.reltol))) | ||
| end | ||
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| integrator.u = u | ||
| end | ||
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| @muladd function perform_step!(integrator,cache::LambaEMCache,f=integrator.f) | ||
| @unpack du1,du2,K,tmp,L,gtmp = cache | ||
| @unpack t,dt,uprev,u,W,p = integrator | ||
| integrator.f(du1,uprev,p,t) | ||
| integrator.g(L,uprev,p,t) | ||
| @. K = @muladd uprev + dt*du1 | ||
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| if is_diagonal_noise(integrator.sol.prob) | ||
| @. tmp=L*W.dW | ||
| else | ||
| A_mul_B!(tmp,L,W.dW) | ||
| end | ||
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| @. u = K+tmp | ||
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| if integrator.opts.adaptive | ||
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| if !is_diagonal_noise(integrator.sol.prob) | ||
| g_sized = norm(L,2) | ||
| else | ||
| g_sized = L | ||
| end | ||
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| @. tmp = @muladd K + L*integrator.sqdt | ||
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| if !is_diagonal_noise(integrator.sol.prob) | ||
| integrator.g(gtmp,tmp,p,t) | ||
| g_sized2 = norm(gtmp,2) | ||
| @. tmp = dW.^2 - dt | ||
| diff_tmp = integrator.opts.internalnorm(tmp) | ||
| En = (g_sized2-g_sized)/(2integrator.sqdt)*diff_tmp | ||
| @. tmp = En | ||
| else | ||
| integrator.g(gtmp,tmp,p,t) | ||
| @. tmp = (gtmp-L)/(2integrator.sqdt)*(W.dW.^2 - dt) | ||
| end | ||
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| # Ed | ||
| integrator.f(du2,K,p,t+dt) | ||
| @. tmp += integrator.opts.internalnorm(dt*(du2 - du1)/2) | ||
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| @tight_loop_macros for (i,atol,rtol) in zip(eachindex(u),Iterators.cycle(integrator.opts.abstol),Iterators.cycle(integrator.opts.reltol)) | ||
| @inbounds tmp[i] = (tmp[i])/(atol + max(abs(uprev[i]),abs(u[i]))*rtol) | ||
| end | ||
| integrator.EEst = integrator.opts.internalnorm(tmp) | ||
| end | ||
| end | ||
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| @muladd function perform_step!(integrator,cache::LambaEulerHeunConstantCache,f=integrator.f) | ||
| @unpack t,dt,uprev,u,W,p = integrator | ||
| du1 = integrator.f(uprev,p,t) | ||
| K = @muladd uprev + dt*du1 | ||
| L = integrator.g(uprev,p,t) | ||
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| if is_diagonal_noise(integrator.sol.prob) | ||
| noise = L.*W.dW | ||
| else | ||
| noise = L*W.dW | ||
| end | ||
| tmp = @muladd K+L*W.dW | ||
| gtmp2 = (1/2).*(L.+integrator.g(tmp,p,t+dt)) | ||
| if is_diagonal_noise(integrator.sol.prob) | ||
| noise2 = gtmp2.*W.dW | ||
| else | ||
| noise2 = gtmp2*W.dW | ||
| end | ||
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| u = @muladd uprev + (1/2)*dt*(du1+integrator.f(tmp,p,t+dt)) + noise2 | ||
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| if integrator.opts.adaptive | ||
| du2 = integrator.f(K,p,t+dt) | ||
| Ed = dt*(du2 - du1)/2 | ||
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| utilde = uprev + L*integrator.sqdt | ||
| ggprime = (integrator.g(utilde,p,t).-L)./(integrator.sqdt) | ||
| En = ggprime.*(W.dW.^2)./2 | ||
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| integrator.EEst = integrator.opts.internalnorm((Ed + En)/((integrator.opts.abstol + max.(abs(uprev),abs(u))*integrator.opts.reltol))) | ||
| end | ||
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| integrator.u = u | ||
| end | ||
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| @muladd function perform_step!(integrator,cache::LambaEulerHeunCache,f=integrator.f) | ||
| @unpack du1,du2,K,tmp,L,gtmp = cache | ||
| @unpack t,dt,uprev,u,W,p = integrator | ||
| integrator.f(du1,uprev,p,t) | ||
| integrator.g(L,uprev,p,t) | ||
| @. K = @muladd uprev + dt*du1 | ||
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| if is_diagonal_noise(integrator.sol.prob) | ||
| @. tmp=L*W.dW | ||
| else | ||
| A_mul_B!(tmp,L,W.dW) | ||
| end | ||
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| @. tmp = K+tmp | ||
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| integrator.f(du2,tmp,p,t+dt) | ||
| integrator.g(gtmp,tmp,p,t+dt) | ||
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| if is_diagonal_noise(integrator.sol.prob) | ||
| #@. nrtmp=(1/2)*W.dW*(gtmp1+gtmp2) | ||
| @tight_loop_macros for i in eachindex(u) | ||
| @inbounds dWo2 = (1/2)*W.dW[i] | ||
| @inbounds tmp[i]=dWo2*(L[i]+gtmp[i]) | ||
| end | ||
| else | ||
| #@. gtmp1 = (1/2)*(gtmp1+gtmp2) | ||
| @tight_loop_macros for i in eachindex(gtmp) | ||
| @inbounds gtmp[i] = (1/2)*(L[i]+gtmp[i]) | ||
| end | ||
| A_mul_B!(tmp,gtmp,W.dW) | ||
| end | ||
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| dto2 = dt*(1/2) | ||
| #@. u = @muladd uprev + dto2*(ftmp1+ftmp2) + nrtmp | ||
| @tight_loop_macros for i in eachindex(u) | ||
| @inbounds u[i] = @muladd uprev[i] + dto2*(du1[i]+du2[i]) + tmp[i] | ||
| end | ||
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| if integrator.opts.adaptive | ||
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| if !is_diagonal_noise(integrator.sol.prob) | ||
| g_sized = norm(L,2) | ||
| else | ||
| g_sized = L | ||
| end | ||
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| @. tmp = @muladd uprev + L*integrator.sqdt | ||
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| if !is_diagonal_noise(integrator.sol.prob) | ||
| integrator.g(gtmp,tmp,p,t) | ||
| g_sized2 = norm(gtmp,2) | ||
| @. tmp = dW.^2 | ||
| diff_tmp = integrator.opts.internalnorm(tmp) | ||
| En = (g_sized2-g_sized)/(2integrator.sqdt)*diff_tmp | ||
| @. tmp = En | ||
| else | ||
| integrator.g(gtmp,tmp,p,t) | ||
| @. tmp = (gtmp-L)/(2integrator.sqdt)*(W.dW.^2) | ||
| end | ||
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| # Ed | ||
| integrator.f(du2,K,p,t+dt) | ||
| @. tmp += integrator.opts.internalnorm(dt*(du2 - du1)/2) | ||
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| @tight_loop_macros for (i,atol,rtol) in zip(eachindex(u),Iterators.cycle(integrator.opts.abstol),Iterators.cycle(integrator.opts.reltol)) | ||
| @inbounds tmp[i] = (tmp[i])/(atol + max(abs(uprev[i]),abs(u[i]))*rtol) | ||
| end | ||
| integrator.EEst = integrator.opts.internalnorm(tmp) | ||
| end | ||
| end |
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