Abstract
Fractional differential equations provide a tractable mathematical framework to describe anomalous behavior in complex physical systems, yet they introduce new sensitive model parameters, i.e. derivative orders, in addition to model coefficients. We formulate a sensitivity analysis of fractional models by developing a fractional sensitivity equation method. We obtain the adjoint fractional sensitivity equations, in which we present a fractional operator associated with logarithmic-power law kernel. We further construct a gradient-based optimization algorithm to compute an accurate parameter estimation in fractional model construction. We develop a fast, stable, and convergent Petrov–Galerkin spectral method to numerically solve the coupled system of original fractional model and its corresponding adjoint fractional sensitivity equations.









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This work was supported by the AFOSR Young Investigator Program (YIP) Award (FA9550-17-1-0150).
Appendices
Proof of Lemma 1
Part A: \(\sigma \in (0,1)\). We start from the \(RL-PL\) definition, given in (8).
Part B: \(\sigma \in (1,2)\). Similarly, we start from the \(RL-PL\) definition, given in (8).
Proof of Lemma 2
In Lemma 2.1 in [49] and also in [64], it is shown that \(\Vert \cdot \Vert _{{^l}H^{\sigma }_{}({\varLambda })}\) and \(\Vert \cdot \Vert _{{^r}H^{\sigma }_{}({\varLambda })}\) are equivalent. Therefore, for \(u \in H^{\sigma }_{}({\varLambda })\), there exist positive constants \(C_1\) and \(C_2\) such that
which leads to
where \(\tilde{C}_1\) is a positive constant. Similarly, we can show that \(\Vert u \Vert _{{^c}H^{\sigma }_{}({\varLambda })}^2 \le \tilde{C}_2 \, \Vert u \Vert _{{}H^{\sigma }_{}({\varLambda })}\), where \(\tilde{C}_2\) is a positive constant.
Proof of Lemma 4
\(\mathcal {X}_1\) is endowed with the norm \(\Vert \cdot \Vert _{\mathcal {X}_1}\), where \(\Vert \cdot \Vert _{\mathcal {X}_1}\equiv \Vert \cdot \Vert _{{^c}H^{\beta _1/2}_{}({\varLambda }_1)}\) by Lemma 2. Moreover, \(\mathcal {X}_2\) is associated with the norm
where
and
We use the mathematical induction to carry out the proof. Therefore, we assume the following equality holds
Since,
and
we can show that
Proof of Lemma 7
According to [57], we have \({}_{a_i}^{}{\mathcal {D}}_{x_i}^{\beta _i} u={}_{a_i}^{}{\mathcal {D}}_{x_i}^{\beta _i/2} ({}_{a_i}^{}{\mathcal {D}}_{x_i}^{\beta _i/2} u)\) and \({}_{x_i}^{}{\mathcal {D}}_{b_i}^{\beta _i/2} u={}_{x_i}^{}{\mathcal {D}}_{b_i}^{\beta _i/2}({}_{x_i}^{}{\mathcal {D}}_{b_i}^{\beta _i/2} u)\). Let \(\bar{u}={}_{a_i}^{}{\mathcal {D}}_{x_i}^{\beta _i/2} u\). Then,
Based on the homogeneous boundary conditions, \(\left\{ \frac{v}{{\varGamma }(1-\beta _i/2)\int _{a_i}^{x_i} \frac{\bar{u}ds}{(x_i-s)^{\beta _i/2}}} \right\} ^{b_i}_{x_i=a_i}=0.\) Therefore,
Moreover, we find that
Therefore, we get
Proof of Lemma 8
We know that
Therefore, by Hölder inequality
Moreover, by equivalence of \(\vert \cdot \vert _{H^{s}(I)} \equiv \vert \cdot \vert ^{*}_{H^{s}(I)} = \vert \cdot \vert ^{1/2}_{{^l}H^{s}(I)} \vert \cdot \vert ^{1/2}_{{^r}H^{s}(I)} \) we have
where \(0<\tilde{\beta }_1\le 1\). Therefore,
where \(0<\tilde{\beta }_2\le 1\) and \(0<\bar{\beta }\).
Proof of the Stability Theorem 5
Part A: \(d=1\). It is evident that u and v are in Hilbert spaces (see [49, 64]). For \(0< \tilde{\beta } \le 1\), we have
since \({\sup }_{{u \in U}} \vert a(u , v)\vert >0\). Next, by equivalence of spaces and their associated norms, (63), and (64), we obtain
and
where \(C_1\), \(C_2\), and \(C_3\) are positive constants. Therefore,
where \(\tilde{C}\) is \(min\{C_1, \, C_2, \, C_3 \}\). Also, the norm \( \Vert u \Vert _{U} \, \Vert v \Vert _{V}\) is equivalent to the right hand side of inequality (93). Therefore, \(\vert a(u,v)\vert \ge C \, \Vert u \Vert _{U}\Vert v \Vert _{V}\).
Part B: \(d > 1\). Similarly, we have
where \(0< \beta \le 1\). Recalling that as the direct consequences of (63), we obtain
Thus,
for \(u,\, v \in L^2(I; \mathcal {X}_d)\), where \(0<\tilde{C}\) and \(0<\tilde{\beta }\le 1\). Furthermore, Lemma 8 yields
Therefore, from (95) and (96) we have
where
for \(u \in U \), \(v \in U\) and \(0<\tilde{C}_2\le 1\). By considering (97) and (98), we get
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Kharazmi, E., Zayernouri, M. Fractional Sensitivity Equation Method: Application to Fractional Model Construction. J Sci Comput 80, 110–140 (2019). https://doi.org/10.1007/s10915-019-00935-0
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DOI: https://doi.org/10.1007/s10915-019-00935-0