robust simulation of transient pde models under uncertainty · dimensionless radial position. r....
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Robust Simulation of Transient PDE Models under Uncertainty
Chenyu Wang, Matthew D. Stuber
2019 AIChE Annual MeetingOrlando, FL, November 14th
Robust Simulation
1
Robust simulation
ODE IVPs
Finite difference method
Differential inequalities
Taylor series expansion
Rigorous bounds
Rigorous bounds
Robust simulation is the ability to calculate rigorous bounds on the system’s states for all realizations of parametric uncertainty
[1] M. D. Stuber and P. I. Barton, “Robust simulation and design using semi-infinite programs with implicit functions,” International Journal of Reliability and Safety, vol. 5, no. 3-4, pp. 378-397, 2011.[2] J. K. Scott and P. I. Barton, “Bounds on the reachable sets of nonlinear control systems,” Automatica, vol. 49, no. 1, pp. 93-100, 2013.[3] G. F. Corliss, “Survey of interval algorithms for ordinary differential equations,” Applied Mathematics and Computation, vol. 31, pp. 112-120, 1989.
PDE IBVPs
Reacting flow systems
Thermofluidsystems
Drug delivery
Parametric Partial Differential Equations
• Provide valid and efficient bounds for parametric PDE system (IBVP)
2
• Assumptions:
― is locally Lipschitz continuous;
― For every , there exists a unique solution over the time domain .
• Objective: efficiently compute bounds on the reachable set with less conservatism.
• Reachable set: .
Interval Arithmetic and Affine Arithmetic
Interval Arithmetic (IA) Affine Arithmetic (AA)
•• Dependency problems: overestimation
• Keep track of the dependency;• Enhance the efficacy of interval operations
[4] J. Comba, J. Stolfi, “Affine arithmetic and its applications to computer graphics,” anais do vii sibgrapi, 9-18 (1993).[5] J. Stolfi, L. H. de Figueiredo, “An introduction to affine arithmetic,” Trends in Applied and ComputationalMathematics, 4 (3) (2003) 297–312.[6] L. H. De Figueiredo, J. Stolfi, “Affine arithmetic: concepts and applications,” Numerical Algorithms, 37 (1-4) (2004) 147–158. 3
Physical bounds G
Differential Inequalities
1 0 0( , ), {0,..., , }
k k k kh k x x f x x cp
1, , 0, 0,
1, , 0, 0,
([ , ], ),
([ , ], ),
L L L L U L Lk i k i i k k i iU L Uk i k i i k k i i
U U U U
x
x
x hf P c
x hf cP
x
x
x x
x x1, , 0, 0,
1, , 0, 0,
( [ , ], ),
( [ , ], ),
L L L L L
U U U U
U L Lk i k i i i k k i iU U L Uk i k i i i k k i i
x P
x
x hf x c
x hf x cP
x x
x x
0 0 0
( )( ( ), ( )), ( , ], [)
f
d tt t t
dtt t
t
xf x cpx
[ ] :([ , ]) { , }
([ , ]) { , }[ ] :
L L U L U Li i iU L U L U Ui i i
z x
xz
x x z xx
x x xxz
1, , 0, 0,
1, , 0, 0,
( ( ([ , ])), ),
( ( ([ , ])), ),
L L L L L U L Lk i k i i G i k k i iU U L Uk i k i i G i k k i i
U U U U
x P
x
x hf x c
x hf xP c
x x
x x
( ) ( ) , with
xnGX G X X X
X G∩ ⊂ ⊂ ∀ ⊂
∩ =∅/
Discrete-time DI
[7] X. Yang and J. K. Scott, “Efficient reachability bounds for discrete-time nonlinear systems by extending the continuous-time theory of differential inequalities," in 2018 Annual American Control Conference (ACC), pp. 6242-6247, IEEE, 2018.
( ) pnPt p
4
ODE IVPs
Explicit Euler
Interval extension Flattening operators Interval refinement operators
Bounding PDE
5
PDE IBVP
Bounds
Spatial FDM
DI
IA
AA
Discrete-time integration
? System of ODE IVPs
Continuous space state variable
Discrete space state variables
Continuous-time integration
?
Convection-Diffusion System
Centered finite difference Forward finite difference Backward finite difference
t = 0.5
6
Convection-Diffusion System
Centered finite difference Forward finite difference Backward finite difference
y = 0.5
7
Convection-Reaction System
Centered finite difference Forward finite difference Backward finite difference
t = 0.5
8
Convection-Reaction System
Centered finite difference Forward finite difference Backward finite difference
y = 0.5
9
Diffusion-Reaction System
t = 0.5 y = 0.5
10
Coupled IBVPs
[8] J. Makungu, H. Haario, W. C. Mahera, “A generalized 1-dimensional particle transport method for convection diffusion reaction model,” Afrika Matematika, 23 (1) (2012) 21–39. 11
Coupled IBVPs
12
Transport Model in Tumor
22
2 ( )s spp pRα
∇ = −
udr
K dp= −
( )
( ) ( )
v
v p v
rSr pL pV
φ
φ
=∇⋅
= −
u
• Fluid Transport Model– Darcy’s law– Continuity equation
• Solute Transport Model2
22 2
1 ( 1 () ) sC r uC CD rt r r r r r
φ∂ ∂ ∂ ∂+ = +
∂ ∂ ∂ ∂Convection Diffusion
• Pore Theory2
8o
prLL
γµ
= oHDPL
γ= 1 Wσ = −
[9] J. D. Martin, M. Panagi, C. Wang, T. T. Khan, M. R. Martin, C. Voutouri, K. Toh, P. Papageorgis,F. Mpekris, C. Polydorou, et al., “Dexamethasone increases cisplatin-loaded nanocarrier delivery and efficacy in metastatic breast cancer by normalizing the tumor microenvironment,” ACS nano.
13
Transport Model in Tumor
[2.75E-7,2.85E-7]K ∈ [1.65E-6,1.75E-6]pL ∈
14
Dimensionless Radial Position r
CD
imen
sion
less
Con
cent
ratio
n
Concentration at 30 min post injection
Bounds DI
IA
FDMPDE
System of ODE IVPs
?
AAG
Concentration at medium radius
Transport Model in Tumor
[2.75E-7,2.85E-7]K ∈ [1.65E-6,1.75E-6]pL ∈
15C
Dim
ensi
onle
ss C
once
ntra
tion
Time (s)Bounds DI
IA
FDMPDE
System of ODE IVPs
?
AAG
Transport Model in Tumor
16
Dimensionless Radial Position r
CD
imen
sion
less
Con
cent
ratio
n
Concentration at 30 min post injectionMethod Time (s)
Exact solutions 0.156898524
FDM using IA and DI 4.366923814
FDM using IA and DI with G 212.759461536
FDM using AA and DI 4.033772619
FDM using AA and DI with G 201.610427039
0 0.02 0.04 0.06 0.08r
-0.4
-0.2
0
0.2
0.4
0.6
c
Summary
Backward finite difference
Conv-Diff Conv-Rxn Diff-Rxn Tumor Model
FD VIA=VAA VIA=VAA Unstable
BD VIA=VAA VIA=VAA VIA>VAA
CD VIA>VAA VIA>VAA VIA>VAA Unstable
Interval Extension 0 0.2 0.4 0.6 0.8 1
y
-6
-4
-2
0
2
4
6
x
10 148
Exact solutionsFDM using AA
0 0.2 0.4 0.6 0.8 1
y
-8
-6
-4
-2
0
2
4
6
8
x
10 217
Exact solutionsFDM using IA
17
Acknowledgements Group members
• Prof. Matthew D. Stuber, • Matthew Wilhelm, • William Hale• Other group members
Funding• University of Connecticut• National Science Foundation, Award No.: 1560072, 1706343, 1932723Any opinions, finding and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation
Any questions?
Backup Slides
Discrete-time Differential Inequalities
1 0 0( , ), {0,..., , }
k k k kh k x x f x x c
1, , 0, 0,
1, , 0, 0,
([ , ], ),
([ , ], ),
L L L L U L Lk i k i i k k i iU L Uk i k i i k k i i
U U U U
x
x
x hf x c
x hf x c
x x
x x
1, , 0, 0,
1, , 0, 0,
( [ , ], ),
( [ , ], ),
L L L L L U L Lk i k i i i k k i iU U L Uk i k i i i k k i i
U U U U
x
x
x hf x c
x hf x c
x x
x x
0 0 0
( )( ( ), ( )), ( , ], [)
f
d tt t t
dtt t
t
xf x x c
[ ] :([ , ]) { , }
([ , ]) { , }[ ] :
L L U L U Li i iU L U L U Ui i i
z x
xz
x x z xx
x x xxz
Explicit Euler
Interval extension
Flattening operators
• For continuous-time system, a trajectory cannot leave the enclosure without crossing its boundary
• Thus, at any , it is only necessary for to decrease faster than all trajectories that are already incident on the ith lower face of .
0( ; , )tx c
[ ( ), ( )]UL t tx x
0[ ],
ftt t ( )L
ix t
0( ; , )
ix t c
[ ( ), ( )]UL t tx x
1, , 0, 0,
1, , 0, 0,
( ( ([ , ])), ),
( ( ([ , ])), ),
L L L L L U L Lk i k i i G i k k i iU U L Uk i k i i G i k
Ui
U Ui
Uk
x
x
x hf x c
x hf x c
x x
x x
Prior enclosure G
Example: [ ] {[1 5],[2, 4],, , [1,7]}L U x x
2
2
{[1 5],[2,2],[1,7]}
{[1 5],[4, 4],[1,
([ , ]) ,
( 7]}[ , ]) ,
L L U
U L U
x x
x x
I.C. and B.C.
Coupled IBVPs
[8] J. Makungu, H. Haario, W. C. Mahera, “A generalized 1-dimensional particle transport method for convection diffusion reaction model,” Afrika Matematika, 23 (1) (2012) 21–39. 11
Coupled IBVPs
[8] J. Makungu, H. Haario, W. C. Mahera, “A generalized 1-dimensional particle transport method for convection diffusion reaction model,” Afrika Matematika, 23 (1) (2012) 21–39. 11
Discrete-time Differential Inequalities
1 0 0( , ), {0,..., , }
k k k kh k x x f x x c
1, , 0, 0,
1, , 0, 0,
([ , ], ),
([ , ], ),
L L L L U L Lk i k i i k k i iU L Uk i k i i k k i i
U U U U
x
x
x hf x c
x hf x c
x x
x x
1, , 0, 0,
1, , 0, 0,
( [ , ], ),
( [ , ], ),
L L L L L U L Lk i k i i i k k i iU U L Uk i k i i i k k i i
U U U U
x
x
x hf x c
x hf x c
x x
x x
3
Discrete-time differential inequalities
Tight enclosure
Discrete-time Differential inequalities
0 0 0
( )( ( ), ( )), ( , ], [)
f
d tt t t
dtt t
t
xf x x c
[ ] :([ , ]) { , }
([ , ]) { , }[ ] :
L L U L U Li i iU L U L U Ui i i
z x
xz
x x z xx
x x xxz
Explicit Euler
Interval extension
Flattening operators
Global optimization
[9] X. Yang and J. K. Scott, “Efficient reachability bounds for discrete-time nonlinear systems by extending the continuous-time theory of differential inequalities," in 2018 Annual American Control Conference (ACC), pp. 6242-6247, IEEE, 2018.
Discrete-time DI vs Continuous-time DI
Discrete-time methods Time per step Time
exact 0.0001506623 0.035599025
Interval 0.001027332844 0.241191195
SDI 0.000212136452 0.048447647
IGB 0.0005043008 0.122923914
BIG 0.000196413496 0.052653721
MG 0.000389502404 0.096926013
Continuous-time methods
Time Steps Time per step Literature
exact 0.167765429 44 0.0038128507 0.004
Interval 0.28842996 63 0.0045782533
SDI 0.374489244 1160 0.0003228356 0.014
MG 0.097186758 64 0.0015185431 0.055
Hukuhara = Affine[ , ], [ , ]L U ULX x x Y y y= =
, ,2 2
,2 2
L U U L L U U L
X X Y Y
X X
Y Y
x x x x y y y ym m
r
r r
mr
XY m
=
=
+ − + −= = = =
+
+
when ( ) ( )
(
(
) ( )
) ( )2 2 2 2
1 1) ( )2 21 1) [ ,
( [ 1,1]
(2 2
X Y
X X Y Y
X Y X YL U L U U L U L
L L U U U U L L
L L U U U U L L U U L
r rm r r
x
r rX Y m
x y y x x y y
x y x y x y x y
x y x y x y x
m
y x y x y
m+ +− + −
+ + − −− + −
− + − + − − +
− + − + − + +
≥
− = −=
=
+
=
−= −
−
−
]
[ , ]
L
L L U Ux y x y− −=
when ( ) ( )
(
(
) ( )
) ( )2 2 2 2
1 1) ( )2 21 1) [ ,
( [ 1,1]
(2 2
X Y
X X Y Y
X Y X YL U L U U L U L
L L U U U U L L
L L U U U U L L U U L
r rm r r
x
r rX Y m
x y y x x y y
x y x y x y x y
x y x y x y x
m
y x y x y
m+ +− + −
+ + − −− + −
− + − + − − +
− + − + − −
<
− = −=
=
= −
= + − + + −
]
[ , ]
L
U U L Lx y x y−= −