Jump to content
Main menu
Main menu
move to sidebar
hide
Navigation
Main page
Recent changes
Random page
freem
Search
Search
Appearance
Create account
Log in
Personal tools
Create account
Log in
Pages for logged out editors
learn more
Contributions
Talk
Editing
Openai/6935580c-34e4-8001-9136-7bbdf1499790
(section)
Add languages
Page
Discussion
English
Read
Edit
Edit source
View history
Tools
Tools
move to sidebar
hide
Actions
Read
Edit
Edit source
View history
General
What links here
Related changes
Special pages
Page information
Appearance
move to sidebar
hide
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
=== This is the standard route when you work in a Banach space XXX chosen so the nonlinear term is well behaved (typical choice: X=Hs(R3)X=H^s(\mathbb R^3)X=Hs(R3) with s>3/2s>3/2s>3/2, so HsH^sHs is an algebra and P∇ ⋅(u⊗u)P\nabla\!\cdot(u\otimes u)P∇⋅(u⊗u) maps Hs→Hs−1H^s\to H^{s-1}Hs→Hs−1, etc.). I will present a general abstract template you can instantiate for a concrete XXX. === Assume: # Gt=eνtΔG_t=e^{\nu t\Delta}Gt=eνtΔ is a strongly continuous semigroup on XXX and there exists β∈[0,1)\beta\in[0,1)β∈[0,1) and constant CGC_GCG such that for all t∈(0,T]t\in(0,T]t∈(0,T] ∥Gt∥Y→X≤CG t−β,\|G_t\|_{Y\to X} \le C_G\, t^{-\beta},∥Gt∥Y→X≤CGt−β, for some Banach space YYY into which P∇ ⋅(⋅⊗⋅)P\nabla\!\cdot(\cdot\otimes\cdot)P∇⋅(⋅⊗⋅) maps (we allow Y=XY=XY=X in many cases). # The bilinear map B(u,v):=P∇ ⋅(u⊗v)B(u,v):=P\nabla\!\cdot(u\otimes v)B(u,v):=P∇⋅(u⊗v) satisfies a product estimate ∥B(u,v)∥Y≤CB∥u∥X∥v∥X.\|B(u,v)\|_{Y} \le C_B \|u\|_X\|v\|_X.∥B(u,v)∥Y≤CB∥u∥X∥v∥X. (These two hypotheses are standard heat-kernel + algebraic embedding facts; for X=HsX=H^sX=Hs with s>32s>\tfrac32s>23 one gets β=0\beta=0β=0 and such an estimate with Y=Hs−1Y=H^{s-1}Y=Hs−1.) Define the Picard map T\mathcal TT on the Banach space C([0,T];X)C([0,T];X)C([0,T];X) with sup norm ∥w∥CTX=sup0≤t≤T∥w(t)∥X\|w\|_{C_TX}=\sup_{0\le t\le T}\|w(t)\|_X∥w∥CTX=sup0≤t≤T∥w(t)∥X by (Tw)(t):=Gtu0−∫0tGt−sB(w(s),w(s)) ds.(\mathcal T w)(t) := G_t u_0 - \int_0^t G_{t-s} B(w(s),w(s))\,ds.(Tw)(t):=Gtu0−∫0tGt−sB(w(s),w(s))ds. Estimate T\mathcal TT on a ball. For any w∈C([0,T];X)w\in C([0,T];X)w∈C([0,T];X), ∥∫0tGt−sB(w,w)(s) ds∥X≤∫0t∥Gt−s∥Y→X ∥B(w,w)(s)∥Y ds≤CGCB∫0t(t−s)−β ∥w∥CTX2 ds=CGCB∥w∥CTX2⋅t1−β1−β.\begin{aligned} \Big\|\int_0^t G_{t-s}B(w,w)(s)\,ds\Big\|_X &\le \int_0^t \|G_{t-s}\|_{Y\to X}\, \|B(w,w)(s)\|_Y\,ds\\ &\le C_G C_B \int_0^t (t-s)^{-\beta}\, \|w\|_{C_TX}^2\, ds\\ &= C_G C_B \|w\|_{C_TX}^2 \cdot \frac{t^{1-\beta}}{1-\beta}. \end{aligned}∫0tGt−sB(w,w)(s)dsX≤∫0t∥Gt−s∥Y→X∥B(w,w)(s)∥Yds≤CGCB∫0t(t−s)−β∥w∥CTX2ds=CGCB∥w∥CTX2⋅1−βt1−β. Taking supremum over t∈[0,T]t\in[0,T]t∈[0,T], ∥Tw∥CTX≤∥u0∥X+CGCB1−β T1−β ∥w∥CTX2.\|\mathcal T w\|_{C_TX} \le \|u_0\|_X + \frac{C_G C_B}{1-\beta}\, T^{1-\beta}\,\|w\|_{C_TX}^2.∥Tw∥CTX≤∥u0∥X+1−βCGCBT1−β∥w∥CTX2. Now pick R=2∥u0∥XR=2\|u_0\|_XR=2∥u0∥X and choose T>0T>0T>0 small so that CGCB1−β T1−β R≤12.\frac{C_G C_B}{1-\beta}\, T^{1-\beta}\,R \le \tfrac12.1−βCGCBT1−βR≤21. Then for every www with ∥w∥CTX≤R\|w\|_{C_TX}\le R∥w∥CTX≤R we have ∥Tw∥CTX≤R\|\mathcal T w\|_{C_TX}\le R∥Tw∥CTX≤R; i.e. T\mathcal TT maps the closed ball BR⊂C([0,T];X)B_R\subset C([0,T];X)BR⊂C([0,T];X) into itself. Contraction estimate: for w,w~∈BRw,\tilde w\in B_Rw,w~∈BR, ∥Tw−Tw~∥CTX≤supt≤T∫0t∥Gt−s∥Y→X ∥B(w,w)−B(w~,w~)∥Y ds≤CGT1−β1−β sups≤T∥B(w+w, w−w~)∥Y≤CGCBT1−β1−β (∥w∥CTX+∥w~∥CTX)∥w−w~∥CTX≤2CGCBT1−βR1−β ∥w−w~∥CTX.\begin{aligned} \|\mathcal T w - \mathcal T\tilde w\|_{C_TX} &\le \sup_{t\le T}\int_0^t \|G_{t-s}\|_{Y\to X}\,\|B(w,w)-B(\tilde w,\tilde w)\|_Y\,ds\\ &\le C_G \frac{T^{1-\beta}}{1-\beta}\, \sup_{s\le T}\|B(w+w,\;w-\tilde w)\|_Y\\ &\le \frac{C_G C_B T^{1-\beta}}{1-\beta}\,( \|w\|_{C_TX}+\|\tilde w\|_{C_TX})\|w-\tilde w\|_{C_TX}\\ &\le \frac{2C_G C_B T^{1-\beta}R}{1-\beta}\,\|w-\tilde w\|_{C_TX}. \end{aligned}∥Tw−Tw~∥CTX≤t≤Tsup∫0t∥Gt−s∥Y→X∥B(w,w)−B(w~,w~)∥Yds≤CG1−βT1−βs≤Tsup∥B(w+w,w−w~)∥Y≤1−βCGCBT1−β(∥w∥CTX+∥w~∥CTX)∥w−w~∥CTX≤1−β2CGCBT1−βR∥w−w~∥CTX. By the choice of TTT this Lipschitz factor is <1<1<1, so T\mathcal TT is a contraction on BRB_RBR. Banach fixed point yields a unique fixed point u∈BR⊂C([0,T];X)u\in B_R\subset C([0,T];X)u∈BR⊂C([0,T];X). In particular u∈C([0,T];X)⊂L∞(0,T;X),u\in C([0,T];X)\subset L^\infty(0,T;X),u∈C([0,T];X)⊂L∞(0,T;X), and the L∞L^\inftyL∞-bound is ∥u∥L∞(0,T;X)≤R=2∥u0∥X\|u\|_{L^\infty(0,T;X)}\le R=2\|u_0\|_X∥u∥L∞(0,T;X)≤R=2∥u0∥X. Remarks: * This shows the L∞(0,T;X)L^\infty(0,T;X)L∞(0,T;X) property is not an independent assumption but a consequence of local existence via Picard. * The smallness requirement is on TTT (local existence). Global existence requires additional a-priori estimates (energy, dissipation, etc.) to extend the time of existence.
Summary:
Please note that all contributions to freem are considered to be released under the Creative Commons Attribution-ShareAlike 4.0 (see
Freem:Copyrights
for details). If you do not want your writing to be edited mercilessly and redistributed at will, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource.
Do not submit copyrighted work without permission!
Cancel
Editing help
(opens in new window)